Pith. sign in

REVIEW 4 major objections 6 minor 116 references

Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook

T0 review · 4 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper defines the trans-domain digital twin by six minimum conditions—two heterogeneous twins, an aligned shared state, at least one coupling, a traceable cross-domain effect, temporal synchronization, and feedback—and argues that merel

desk verdict A serious conceptual framework for cross-domain digital twins, but the central definition is stated inconsistently and the validation is internal, so it is a useful proposal in need of revision more than a settled contribution. read the letter →

arxiv 2607.15908 v1 pith:MMD7XOBN submitted 2026-07-17 cs.SE

Mansoorali Amiri This is my paper · ORCID
classification cs.SE
keywords DigitalTwinTrans-DomainComposite/FederatedTwinsSystem-of-SystemsOperationalCouplingSharedStateMulti-ScaleTemporalCoordinationFeedback-BasedAdaptation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that existing cross-domain digital twin approaches connect domains through data exchange, semantic mapping, and reuse, but do not require that a change in one domain operationally change the state, error, objective, or control of another. It proposes the trans-domain digital twin (TDDT) as a subtype of Composite/Federated Digital Twin System-of-Systems that does require such operational coupling, and supplies a tuple formalism, seven-layer architecture, and minimum compliance conditions to make the distinction testable. The proposal matters because complex systems such as livestock barns, autonomous vehicles, cancer treatment, and fusion reactors have domains that genuinely affect one another, and local optimization can produce whole-system mistakes. If the definition is accepted, engineers get a checklist for building and auditing multi-domain twins that make joint decisions, and researchers get a boundary between interoperability and true cross-domain control. The paper is a conceptual formulation; it explicitly leaves implementation, benchmarking, and field validation to future work.

What carries the argument

The load-bearing mechanism is the fused trans-domain model, Layer 3 of the seven-layer architecture, which re-organizes the outputs of domain-specific twins into three sublayers: an aligned Shared Trans-Domain State (STD), an explicit coupling layer (data, model, state, error, objective, control), and a time-synchronization layer running fast inner, meso, and slow outer loops. The minimum compliance conditions act as the paper's central object — a definitional checklist that a system must pass to be called a TDDT. The compact signature of that checklist is the tuple T_TD = ⟨D, STD, C, L, J, U, K, F⟩, where D is the set of domain twins (at least two), C the couplings, L the temporal policy, J

What would settle it

Exhibit a single deployed composite/federated digital twin that already possesses an aligned shared state, explicit state/error/objective/control coupling, a defined temporal synchronization policy, and feedback-based correction predating this paper — that would refute the claimed gap. Absent that, run the paper's ablation protocol on a TDDT implementation: if removing the shared state or all couplings does not degrade any system-level metric (cost, risk, constraint-violation rate) relative to the full system, the operational value claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that a system is a trans-domain digital twin only when it includes at least two heterogeneous domain twins, an aligned shared state, at least one coupling at the level of state, error, objective, or control, a traceable cross-domain effect, a temporal synchronization policy, and a feedback pathway for correcting the model or coupling. The formalism T_TD = ⟨D, STD, C, L, J, U, K, F⟩ records these ingredients, with the shared state STD aligning time, units, context, quality, uncertainty, constraints, objectives, and control status across domains, and couplings C allowing the output of one domain to change the input, constraint, objective, error, decision, or contro

Load-bearing premise

The central claim rests on the untested empirical premise that existing cross-domain and composite/federated digital twins do not inherently provide operational coupling; if that premise is wrong, the proposed TDDT distinctions lose their claimed novelty.

Editorial extensions

If this is right

  • Engineers can use the six minimum conditions to audit an existing multi-domain twin system and decide whether it is a TDDT or merely an interoperable one; data exchange, an API, or a shared dashboard without operational coupling is explicitly insufficient.
  • The proposed evaluation protocol (ablation of shared state, couplings, temporal coordination, and feedback, plus baselines) gives a concrete way to measure the contribution of operational coupling instead of relying on end-to-end accuracy alone.
  • Standards such as FMI for model exchange and HLA for distributed simulation are assigned the role of encapsulation and scheduling lower-level building blocks, not the coupling mechanism itself, clarifying where they sit relative to a trans-domain twin.
  • The fast-meso-slow loop structure offers a way to coordinate domains that evolve on different timescales, such as an indoor climate loop running in seconds and a livestock growth loop running in weeks, with the meso loop carrying intermediate risk/load signals.
  • Single-episode offline training, where simulators and the fused model run together over one horizon and store knowledge in CRP/SARG/MRG form, could let high-risk applications (health, military, GNSS-independent navigation) start from prior knowledge rather than risky zero-shot online learning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The definition implies a compliance test that the paper leaves implicit: given an implementation, one could algorithmically check the tuple conditions — n≥2, aligned STD, at least one coupling in {state, error, objective, control}, traceable effect, temporal policy, and feedback — and produce a pass/fail verdict; this could become a certification instrument for 'trans-domain' claims.
  • If the TDDT boundary is accepted, it also sharpens the converse claim: many systems currently marketed as multi-domain or federated digital twins would fail the definition, which may pressure vendors to implement feedback and coupling rather than dashboards — an economic consequence the paper does not discuss.
  • The hidden-loop-current index (LCI), defined as the discrepancy between the observed state and a direct prediction after a delay, is a portable diagnostic: it could be applied to any networked simulation or system-of-systems to detect indirect cyclic effects, not just digital twins, suggesting a testable extension beyond the paper's scope.
  • A falsifiable prediction follows from the paper's rationale: in any complex system with cross-domain effects, a TDDT-conformant system should beat a data-exchange-only multi-domain twin on at least one system-level metric (constraint violations, risk, or cost) under the paper's own ablation protocol; this is what future benchmarks would settle.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a conceptual framework for 'Trans-Domain Digital Twin' (TDDT), defined as an operational formulation along the continuum of Composite/Federated Digital Twin System-of-Systems. The core claim is that TDDT goes beyond interoperability, comparison, and reuse by requiring an aligned shared state, explicit couplings among domain states/errors/objectives/controls, temporal coordination, joint decision-making, and feedback-based adaptation. The manuscript contributes a seven-layer architecture, a trans-domain orchestration core (TDOC), minimum compliance conditions, a tuple-based formalism (Section 5), fast/meso/slow temporal loops, a single-episode offline training scheme, a maturity model, a deployment architecture, and proposals for evaluation, safety, provenance, and lifecycle management. The paper is explicitly conceptual and repeatedly states that no benchmark, implementation, or field comparison is provided; it also frames the validation as internal consistency and requirements traceability only.

Significance. If the TDDT definition were made precise and operational, it could provide a useful classification tool for multi-domain digital twin systems and sharpen the distinction between weak interoperability and operational coupling. The paper is commendably explicit about its limitations and proposes a thorough evaluation protocol with baselines, ablation, robustness, and uncertainty testing, which is unusual for a conceptual paper. However, the current manuscript supplies no machine-checked proofs, no reproducible implementation, and no falsifiable predictions; the central evidence is a traceability matrix that maps the author's own requirements to the author's own architecture. The intellectual contribution is therefore conditional: the field would benefit from a rigorous, unambiguous definition and at least one worked demonstration, but neither is yet present.

major comments (4)
  1. [§3.3 vs. §4.1 vs. §5.1] The minimum requirements for a TDDT are stated in at least three non-equivalent forms. Section 3.3 requires 'at least one coupling at the level of state, error, objective, or control' and does not mention joint decision-making. Section 4.1 additionally requires 'multi-domain-based decision-making' and widens couplings to include model coupling. Section 5.1 likewise lists 'multi-domain-based decision-making' as necessary. These predicates mark different classes: a system with two twins, an aligned shared state, state coupling, a temporal policy, and local decisions satisfies Section 3.3 but fails Section 4.1. The paper's central claim—that TDDT is formally distinguishable from CDDT and Composite/Federated DTs—is not testable until a single, unambiguous predicate is given.
  2. [§7.2, Fig. 13] The structural validation maps the paper's own minimum requirements to the paper's own architectural layers and declares each 'Covered.' Because the architecture and the requirements were designed together, this traceability is guaranteed by construction and cannot, by itself, support the claim that the framework captures a meaningful class distinct from existing CDDT or Composite/Federated DTs. The paper's own text says this 'does not prove the correctness, adequacy, or practical performance,' but the passage is presented as 'conceptual validation.' The literature-gap premise—that existing cross-domain and composite systems do not inherently provide operational coupling—is an empirical assertion; it needs a concrete test, e.g., a systematic classification of published CDDT/Composite systems against the proposed predicate, rather than a self-mapping.
  3. [§4.4.6 and §5.4, Eq. (14)] The two runtime mechanisms that make adaptation operational are stipulated, not derived. The Loop Current Index is defined as LCI_i(t, τ) = |x_observed_i(t+τ) − x_direct_i(t+τ)|, which is just a prediction error; no argument shows that this quantity can distinguish a cyclic cross-domain return path from ordinary model error, noise, or exogenous disturbance. Similarly, Route(e_TD) in Eq. (14) requires decomposing the observed error into e_domain, e_alignment, e_coupling, and e_sync, but the paper gives no method for estimating these components from observable quantities or for setting the thresholds. Without such an identifiability argument or a synthetic experiment with known hidden loops, the claimed 'feedback path' cannot be implemented.
  4. [§5.1, Eqs. (5)–(10)] The formalism is a set-theoretic sketch: the alignment operator A, coupling functions C_ij, temporal policy L, update map H, and routing condition Route are all left uninterpreted. Equation (8) introduces weights w_i and regularizers λ_R, λ_U without any associated update law, despite later claims of online weight and constraint adaptation. As a 'general operational formalism,' it needs at least minimal semantic commitments (what constitutes an operational coupling, how timestamps enter L, how C_ij is evaluated from observable data) so that the definition can adjudicate borderline cases. The current notation cannot distinguish the local-decision system of the first major comment from a genuinely joint-decision TDDT.
minor comments (6)
  1. [§1.5] The text of Section 1.5 repeats Section 1.3 almost verbatim; one of the two passage duplicates should be removed or condensed.
  2. [§6.1] The first bullet under 'CDDT' begins with the artifact 'CDDT: First item'; this appears to be leftover template text and should be cleaned up.
  3. [Fig. 2] The table row 'Joint Decision-Making' marks 'Required for trans-domain operation,' which matches Section 4.1 but not Section 3.3. The figure should be reconciled with the final chosen definition, or the inconsistency should be resolved with a note explaining which form is normative.
  4. [§4.4.6] The LCI expression uses LCI_i on both sides of the definition. It should be stated explicitly as, e.g., LCI_i(t, τ) = 1[|x_obs−x_direct| > θ_i], with the threshold and delay horizon defined as configuration parameters.
  5. [§5.2, Eq. (11)] The weights w_i are introduced as free parameters, but no procedure for setting or updating them is given despite the paper's claims about online adaptation. A reference to where such an update would enter (e.g., Layer 7 or the slow loop) would help.
  6. [§5.4, Eq. (14)] The error components e_domain, e_alignment, e_coupling, and e_sync are not defined as measurable quantities. At minimum, the paper should state how they might be estimated (e.g., via dedicated residuals or auxiliary models), even at a conceptual level.

Circularity Check

2 steps flagged · score 3.0 of 10

Conceptual validation is a by-construction traceability check; central definition remains a stipulated proposal with a minor self-citation.

  1. self definitional [Section 7.2, Figure 13]
    "The framework is considered conceptually consistent if: 1. each minimum requirement is addressed by at least one architectural component; ... This mapping only demonstrates the conceptual completeness of the architecture and does not prove the correctness, adequacy, or practical performance of each component."

    The 'validation' is a traceability matrix whose rows are the minimum requirements from Sections 3.3/4.1 and whose columns are the architectural layers (L2, 3.1, 3.2, 3.3, 4/5, 6, 7) that were explicitly designed to realize those requirements. The status 'Covered' is therefore guaranteed by construction: any layer named after a requirement trivially covers it. The paper's own caveat admits that no independent check is performed; the derivation of 'the framework is conceptually consistent' from 'each requirement has a named layer' is a definitional equivalence, not an empirical or logical validation.

  2. self citation load bearing [Section 4.4.3, paragraph after Figure 4]
    "the precise classification of data, model, state, error, objective, decision, and control couplings is the specific formulation of this article for distinguishing TDDT from cross-domain DT [5, 20]."

    The paper states that its own classification of couplings is 'the specific formulation of this article' and then cites [20], the author's own master's thesis, as support for distinguishing TDDT from CDDT. Since [20] is prior work by the same author and not an independent source, this citation does not supply external evidence for the central distinction; the distinction is stipulated in this article. It is a minor load-bearing self-citation because the same paragraph also cites external works [5], and the definition stands on its own.

full rationale

The paper is a conceptual proposal, not an empirical derivation. It makes no fitted predictions, contains no parameter estimation, and does not invoke a uniqueness theorem. The central claim is a stipulated definition of TDDT together with a seven-layer architecture that instantiates that definition. The genuine circular element is Section 7.2's 'conceptual validation': the traceability matrix maps the author's own minimum requirements onto the author's own architectural layers and reports 'Covered,' so the validation is true by construction. This is a real self-referential step, but the paper explicitly discloses that it demonstrates only conceptual completeness and not correctness, weakening its circularity. The self-citation to [20] is present at load-bearing points (e.g., the distinction from CDDT), but external citations also support the same claims, and the definition is stipulated rather than derived from the thesis. Additionally, the minimum requirements are stated non-equivalently in Sections 3.3 and 4.1 (the former omits model-level coupling and multi-domain-based decision-making, the latter includes them), which undermines the claim to a 'formal definition' but is an ambiguity rather than a circularity. Overall, the core contribution remains an independent conceptual framework whose stated limitations are acknowledged; the circularity score reflects the by-construction validation and minor self-citation, not a collapse of the whole derivation into its inputs.

Assumptions & free parameters 4 free parameters · 5 assumptions · 5 invented entities

The central framework rests on several stipulated, untested premises: the existence of a literature gap, the feasibility of state alignment, the sufficiency of the minimum compliance criteria, and the correctness of error routing. The paper introduces multiple new constructs (TDOC, STD, CRP/SARG, LCI) as architectural or algorithmic suggestions, none with independent empirical evidence.

free parameters (4)
  • Domain objective weights w_i (Eq. 11) = unspecified
    Weights for physical, biological, energy, cyber, economic, environmental, risk, and uncertainty objectives are introduced but no method is given to set them.
  • Coupling functions C_ij (Eq. 7) = unspecified
    The strength and form of coupling between domains is a free design choice in the formalism.
  • LCI threshold theta_i (Section 4.4.6) = unspecified
    The threshold for detecting 'hidden loop current' is defined but no calibration or default value is provided.
  • Regularization coefficients lambda_R, lambda_U (Eq. 8) = unspecified
    Regularization weights in the trans-domain objective are listed but not determined.
assumptions (5)
  • domain assumption Existing cross-domain and composite/federated digital twin approaches do not inherently provide operational coupling among domains.
    The entire motivation rests on this claimed gap; the paper does not present a systematic survey proving it. Stated in Sections 1.2 and 3.1.
  • domain assumption Complex multi-domain systems require joint decision-making because local decisions lead to inconsistent or high-risk system-level outcomes.
    Stated in Section 1.2 as justification for TDDT. Plausible but not demonstrated.
  • domain assumption A shared trans-domain state can be aligned across heterogeneous domains with different units, temporal rates, and ontologies.
    Assumed throughout Layer 3 (Section 4.4). Semantic and temporal alignment is asserted as feasible.
  • ad hoc to paper The minimum compliance criteria (two twins, shared state, coupling, traceable effect, temporal policy, feedback) are sufficient for operational trans-domain coupling.
    Defined in Section 3.3 specifically for this paper; no proof that these conditions guarantee the desired behavior.
  • ad hoc to paper Errors can be correctly attributed to Layer 2 (domain model) vs Layer 3 (alignment/coupling) via the Route(eTD) thresholds.
    The routing rule in Eq. 14 is a heuristic proposal; no evidence that thresholds can be reliably set or that attribution is correct.
invented entities (5)
  • Trans-Domain Digital Twin (TDDT)
    purpose: The central proposed concept: a composite/federated DT SoS with mandatory operational coupling.
    A conceptual construct; no external data or falsifiable prediction.
  • Trans-Domain Orchestration Core (TDOC)
    purpose: Orchestrates data exchange, shared state, coupling, synchronization, decision-making, and feedback.
    Described as a logical component; no implementation or empirical handle.
  • Shared Trans-Domain State (STD)
    purpose: The aligned, fused state representation used by all domains.
    A core architectural artifact, not an observable entity.
  • Context Reference Patterns (CRP) / Stage-Aware Reference Guidance (SARG)
    purpose: Offline training reference patterns for single-episode learning.
    Proposed mechanisms; paper states their benefits are research hypotheses (Section 16).
  • Hidden Loop Current / Loop Current Index (LCI)
    purpose: An index to detect cyclic error signatures across domains.
    Formula given but no threshold calibration, no test, and no external validation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook." pith.science (2026). https://pith.science/paper/MMD7XOBN

@misc{pith2026260715908,
  author       = {Pith},
  title        = {Pith review of: Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MMD7XOBN}},
  note         = {Machine review of arXiv:2607.15908}
}
read the original abstract

Complex systems comprise heterogeneous domains whose states, uncertainties, risks, and control consequences can cross domain boundaries. Existing cross-domain digital twin approaches broadly focus on comparison, reuse, semantic mapping, standardization, and interoperability, but do not inherently require operational connections among domain states, errors, objectives, constraints, decisions, and controls. This article proposes the trans-domain digital twin as an operational formulation along the continuum of Composite/Federated Digital Twin Systems. This approach connects heterogeneous domain twins through an aligned shared state, explicit coupling of data, models, states, errors, objectives, and controls, heterogeneous temporal coordination, joint decision-making, and feedback-based adaptation. The proposed framework presents a seven-layer conceptual architecture, a trans-domain orchestration core, minimum compliance conditions, a general operational formalism, progressive fast-meso-slow loops, and a single-episode offline training mechanism linked to bounded online adaptation. It also describes conceptual validation and evaluation criteria, a maturity model, a reference deployment architecture, and requirements for runtime safety, provenance, versioning, and model lifecycle management. The framework is conceptually mappable to standards for digital twins, model exchange, distributed simulation, and smart transducers; however, its formal compliance and operational effectiveness must be examined through independent benchmarks, uncertainty quantification, ablation testing, and field validation.

Figures

Figures reproduced from arXiv: 2607.15908 by the authors.

Figure 1
Figure 1. Layered Architecture of CDDT - Independent domain-specific twins are connected through data exchange, [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Measurable research gap between existing digital twin configurations and the proposed TDDT. This table [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Architectural–algorithmic overview of TDDT - Layer 1 shows the structural view of the real multi-domain [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (17 more)
Figure 4
Figure 4. Figure 4: Proposed classification of operational coupling in TDDT. Data, model, state, error, objective, and control [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Coupled Time Loops in TDDT: The fast loop performs short-term local control; the slow loop updates [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Roles and architectural positions of representative modeling approaches in the fused trans-domain model. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Schematic representation of hidden loop current: The effect of domain ( [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Knowledge-transformation pathway in the proposed single-episode TDDT training process. Fast-loop [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Simplified representation of single-episode learning in the progressive coupled loops of TDDT: At each fast [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Data-processing details in the progressive TDDT loops: the fast path generates the sequence of state [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Offline Error Function and High-Importance Dataset: The outputs of the inner, meso, and outer simulators [PITH_FULL_IMAGE:figures/full_fig_p020_11.png]
Figure 12
Figure 12. Figure 12: Layered Distinction between CDDT and TDDT: On the CDDT side, domain-specific twins are mainly [PITH_FULL_IMAGE:figures/full_fig_p021_12.png]
Figure 13
Figure 13. Figure 13: Conceptual traceability matrix between the minimum TDDT requirements and the corresponding architectural [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]
Figure 14
Figure 14. Figure 14: Illustrative trans-domain walkthrough for a closed livestock environment. Temperature and gas changes [PITH_FULL_IMAGE:figures/full_fig_p023_14.png]
Figure 15
Figure 15. Figure 15: Proposed conceptual acceptance dimensions for TDDT. The criteria assess completeness, coupling traceabil [PITH_FULL_IMAGE:figures/full_fig_p024_15.png]
Figure 16
Figure 16. Figure 16: Proposed maturity levels for TDDT implementations. Levels L0–L2 represent independent, connected, [PITH_FULL_IMAGE:figures/full_fig_p024_16.png]
Figure 17
Figure 17. Figure 17: Proposed reference deployment view of the TDDT architecture. Sensor and actuator data pass through the [PITH_FULL_IMAGE:figures/full_fig_p025_17.png]
Figure 18
Figure 18. Figure 18: Proposed evaluation dimensions and example metrics for TDDT implementations. The dimensions [PITH_FULL_IMAGE:figures/full_fig_p026_18.png]
Figure 19
Figure 19. Figure 19: Proposed runtime-safety, failure-management, and recovery requirements for TDDT. The table links health [PITH_FULL_IMAGE:figures/full_fig_p028_19.png]
Figure 20
Figure 20. Figure 20: Proposed lifecycle of a domain model or coupling in TDDT. A candidate component progresses from [PITH_FULL_IMAGE:figures/full_fig_p029_20.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

116 extracted references · 5 linked inside Pith

  1. [1]

    Glaessgen and D

    Edward H. Glaessgen and D. S. Stargel. The digital twin paradigm for future nasa and u.s. air force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Honolulu, HI, USA, 2012. AIAA. AIAA Paper 2012-1818, Special Session on the Digital Twin

    2012

  2. [2]

    Digital twin in manufacturing: A categorical literature review and classification.IFAC-PapersOnLine, 51(11):1016–1022, 2018

    Werner Kritzinger, Matthias Karner, Georg Traar, Jan Henjes, and Wilfried Sihn. Digital twin in manufacturing: A categorical literature review and classification.IFAC-PapersOnLine, 51(11):1016–1022, 2018

    2018

  3. [3]

    Characterising the digital twin: A systematic literature review.CIRP Journal of Manufacturing Science and Technology, 29(Part A):36–52, 2020

    David Jones, Chris Snider, Aydin Nassehi, Jason Yon, and Ben Hicks. Characterising the digital twin: A systematic literature review.CIRP Journal of Manufacturing Science and Technology, 29(Part A):36–52, 2020

    2020

  4. [4]

    Youn, Michael D

    Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, and Zhen Hu. A comprehensive review of digital twin—part 1: Modeling and twinning enabling technologies.Structural and Multidisciplinary Optimization, 65(12):354, 2022

    2022

  5. [5]

    Structured development of digital twins—a cross-domain analysis towards a unified approach.Processes, 10(8):1490, 2022

    Wolfgang Heindl and Christian Stary. Structured development of digital twins—a cross-domain analysis towards a unified approach.Processes, 10(8):1490, 2022

    2022

  6. [6]

    A cross-domain systematic mapping study on software engineering for digital twins

    Manuela Dalibor, Nico Jansen, Bernhard Rumpe, David Schmalzing, Louis Wachtmeister, Manuel Wimmer, and Andreas Wortmann. A cross-domain systematic mapping study on software engineering for digital twins. Journal of Systems and Software, 193:111361, 2022

    2022

  7. [7]

    Digital twins in smart farming.Agricultural Systems, 189:103046, 2021

    Cor Verdouw, Bedir Tekinerdogan, Adrie Beulens, and Sjaak Wolfert. Digital twins in smart farming.Agricultural Systems, 189:103046, 2021. 33 Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook

    2021

  8. [8]

    Athanasiadis

    Christos Pylianidis, Sjoukje Osinga, and Ioannis N. Athanasiadis. Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 184:105942, 2021

    2021

Show all 116 references
  1. [9]

    Digital twins in agriculture: Orchestration and applications.Journal of Agricultural and Food Chemistry, 72(19):10737–10752, 2024

    Marc Escribà-Gelonch, Shu Liang, Pieter van Schalkwyk, Ian Fisk, Nguyen Van Duc Long, and V olker Hessel. Digital twins in agriculture: Orchestration and applications.Journal of Agricultural and Food Chemistry, 72(19):10737–10752, 2024

    2024

  2. [10]

    Digital twins for the designs of systems: A perspective

    Anton van Beek, Vispi Karkaria, and Wei Chen. Digital twins for the designs of systems: A perspective. Structural and Multidisciplinary Optimization, 66(3):49, 2023

    2023

  3. [11]

    Self-adaptive manufacturing with digital twins.arXiv preprint arXiv:2103.11941, 2021

    Tim Bolender, Gereon Bürvenich, Manuela Dalibor, Bernhard Rumpe, and Andreas Wortmann. Self-adaptive manufacturing with digital twins.arXiv preprint arXiv:2103.11941, 2021

    2021 arXiv

  4. [12]

    Integration challenges for digital twin systems-of-systems

    Judith Michael, Jérôme Pfeiffer, Bernhard Rumpe, and Andreas Wortmann. Integration challenges for digital twin systems-of-systems. InProceedings of the 10th IEEE/ACM International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems, SESoS’22, pages ...

    2022

  5. [13]

    A comprehensive review of digital twin from the perspective of total process: Data, models, networks and applications.Sensors, 23(19):8306, 2023

    Honghai Wu, Pengwei Ji, Huahong Ma, and Ling Xing. A comprehensive review of digital twin from the perspective of total process: Data, models, networks and applications.Sensors, 23(19):8306, 2023

    2023

  6. [14]

    A review of urban digital twins integration, challenges, and future directions in smart city development.Sustainability, 16(19):8337, 2024

    Silvia Mazzetto. A review of urban digital twins integration, challenges, and future directions in smart city development.Sustainability, 16(19):8337, 2024

    2024

  7. [15]

    Jiang, M

    Y . Jiang, M. Li, W. Wu, X. Wu, X. Zhang, X. Huang, R. Y . Zhong, and G. G. Q. Huang. Multi-domain ubiquitous digital twin model for information management of complex infrastructure systems.Advanced Engineering Informatics, 56:101951, 2023

    2023

  8. [16]

    Digital twins of the natural environment.Patterns, 2(10):100359, 2021

    Gordon Blair. Digital twins of the natural environment.Patterns, 2(10):100359, 2021

    2021

  9. [17]

    Interoperability of digital twins: Challenges, success factors, and future research directions

    Istvan David, Guodong Shao, Claudio Gomes, Dawn Tilbury, and Bassam Zarkout. Interoperability of digital twins: Challenges, success factors, and future research directions. In Tiziana Margaria and Bernhard Steffen, editors,Leveraging Applications of Formal Methods, Verificatio...

    2025

  10. [18]

    Digital twin: Enabling technologies, challenges and open research.IEEE Access, 8:108952–108971, 2020

    Aidan Fuller, Zhong Fan, Charles Day, and Chris Barlow. Digital twin: Enabling technologies, challenges and open research.IEEE Access, 8:108952–108971, 2020

    2020

  11. [19]

    Towards digital twinning for multi-domain simulation workflows in urban design: A case study in gothenburg

    Alex Gonzalez-Caceres, Franziska Hunger, Jens Forssén, Sanjay Somanath, Andreas Mark, Vasilis Naserentin, Joakim Bohlin, Anders Logg, Beata Wästberg, Dominika Komisarczyk, Fredrik Edelvik, and Alexander Hollberg. Towards digital twinning for multi-domain simulation workflows i...

    2025

  12. [20]

    Vers des jumeaux numériques intelligents en agriculture en environnement contrôlé : contributions conjointes en détection de fruits par vision et en simulation trans-domaines

    Mansoorali Amiri. Vers des jumeaux numériques intelligents en agriculture en environnement contrôlé : contributions conjointes en détection de fruits par vision et en simulation trans-domaines. Mémoire de maîtrise, Université de Montréal, Montréal, Canada, 2025

    2025

  13. [21]

    C. R. Vergara, Georgios Theodoropoulos, Rami Bahsoon, Wilmer Yanez, and Nikos Tziritas. Federated digital twins as an enabling technology for collaborative decision-making. InProceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM-PA...

  14. [22]

    Khedr and John S

    Mennatullah T. Khedr and John S. Fitzgerald. The composition of digital twins for systems-of-systems: A systematic literature review.arXiv preprint arXiv:2506.20435, 2025

    2025 arXiv

  15. [23]

    Gaussian process emulators for computer experiments with inequality constraints.Mathematical Geosciences, 49(5):557–582, 2017

    Hassan Maatouk and Xavier Bay. Gaussian process emulators for computer experiments with inequality constraints.Mathematical Geosciences, 49(5):557–582, 2017

    2017

  16. [24]

    Kennedy and Anthony O’Hagan

    Marc C. Kennedy and Anthony O’Hagan. Bayesian calibration of computer models.Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3):425–464, 2001

    2001

  17. [25]

    An adaptive robust model predictive control for indoor climate optimization and uncertainties handling in buildings.Building and Environment, 163:106326, 2019

    Shiyu Yang, Man Pun Wan, Wanyu Chen, Bing Feng Ng, and Deqing Zhai. An adaptive robust model predictive control for indoor climate optimization and uncertainties handling in buildings.Building and Environment, 163:106326, 2019

    2019

  18. [26]

    Modelica Association Project FMI, 2024

    Modelica Association Project FMI.Functional Mock-up Interface Specification, Version 3.0.2. Modelica Association Project FMI, 2024

    2024

  19. [27]

    Institute of Electrical and Electronics Engineers, 2025

    IEEE.IEEE 1516-2025: IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA)— Framework and Rules. Institute of Electrical and Electronics Engineers, 2025

    2025

  20. [28]

    Conceptualising the digital twin: An analysis of 358 definitions.Digital Twin, 3(1):2600763, 2026

    Ibrahim Yahaya Wuni, Michael Grieves, Muhammad Junaid Yamin, and Saidu Abdulai Koroma. Conceptualising the digital twin: An analysis of 358 definitions.Digital Twin, 3(1):2600763, 2026. 34 Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook

    2026

  21. [29]

    Digital twin in manufac- turing: Conceptual framework and case studies.International Journal of Computer Integrated Manufacturing, 35(8):831–858, 2022

    Igiri Onaji, Divya Tiwari, Payam Soulatiantork, Boyang Song, and Ashutosh Tiwari. Digital twin in manufac- turing: Conceptual framework and case studies.International Journal of Computer Integrated Manufacturing, 35(8):831–858, 2022

    2022

  22. [30]

    Digital twin-driven product design, manufacturing and service with big data.The International Journal of Advanced Manufacturing Technology, 94:3563–3576, 2018

    Fei Tao, Jiangfeng Cheng, Qinglin Qi, Meng Zhang, He Zhang, and Fangyuan Sui. Digital twin-driven product design, manufacturing and service with big data.The International Journal of Advanced Manufacturing Technology, 94:3563–3576, 2018

    2018

  23. [31]

    What is a digital twin?—definitions and insights from an industrial case study in technical product development

    Jakob Trauer, Sebastian Schweigert-Recksiek, Christian Engel, Karoline Spreitzer, and Markus Zimmermann. What is a digital twin?—definitions and insights from an industrial case study in technical product development. InProceedings of the Design Society: DESIGN Conference, vol...

    2020

  24. [32]

    Digital twin: Values, challenges and enablers.arXiv preprint arXiv:1910.01719, 2019

    Adil Rasheed, Omer San, and Trond Kvamsdal. Digital twin: Values, challenges and enablers.arXiv preprint arXiv:1910.01719, 2019

    1910 arXiv

  25. [33]

    Digital twin: Values, challenges and enablers from a modeling perspective.IEEE Access, 8:21980–22012, 2020

    Adil Rasheed, Omer San, and Trond Kvamsdal. Digital twin: Values, challenges and enablers from a modeling perspective.IEEE Access, 8:21980–22012, 2020

    2020

  26. [34]

    Blanes-Vidal, E

    V . Blanes-Vidal, E. Guijarro, S. Balasch, and A. G. Torres. Application of computational fluid dynamics to the prediction of airflow in a mechanically ventilated commercial poultry building.Biosystems Engineering, 100(1):105–116, 2008

    2008

  27. [35]

    Fast and informative flow simulations in a building by using fast fluid dynamics model on graphics processing unit.Building and Environment, 45(3):747–757, 2010

    Wangda Zuo and Qingyan Chen. Fast and informative flow simulations in a building by using fast fluid dynamics model on graphics processing unit.Building and Environment, 45(3):747–757, 2010

    2010

  28. [36]

    Hutmacher and Harmeet Singh

    Dietmar W. Hutmacher and Harmeet Singh. Computational fluid dynamics for improved bioreactor design and 3d culture.Trends in Biotechnology, 26(4):166–172, 2008

    2008

  29. [37]

    Slotnick, Abdollah Khodadoust, Juan J

    Jeffrey P. Slotnick, Abdollah Khodadoust, Juan J. Alonso, David L. Darmofal, William D. Gropp, Elizabeth A. Lurie, and Dimitri J. Mavriplis. A perspective on the state of aerospace computational fluid dynamics technology. Annual Review of Fluid Mechanics, 55:431–457, 2023

    2023

  30. [38]

    Noy and Deborah L

    Natalya F. Noy and Deborah L. McGuinness. Ontology development 101: A guide to creating your first ontology. Technical Report KSL-01-05, Stanford Knowledge Systems Laboratory, Stanford, CA, USA, 2001

    2001

  31. [39]

    Michael Grüninger and Mark S. Fox. Methodology for the design and evaluation of ontologies. InProceedings of the IJCAI-95 Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal, QC, Canada, 1995

    1995

  32. [40]

    Methontology: From ontological art towards ontological engineering

    Mariano Fernández-López, Asunción Gómez-Pérez, and Natalia Juristo. Methontology: From ontological art towards ontological engineering. InProceedings of the AAAI Spring Symposium on Ontological Engineering, AAAI Spring Symposium Series, Stanford, CA, USA, 1997

    1997

  33. [41]

    Towards a methodology for building ontologies

    Mike Uschold and Martin King. Towards a methodology for building ontologies. InProceedings of the IJCAI-95 Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal, QC, Canada, 1995

    1995

  34. [42]

    Springer, Berlin, Heidelberg, 2 edition, 2013

    Jérôme Euzenat and Pavel Shvaiko.Ontology Matching. Springer, Berlin, Heidelberg, 2 edition, 2013

    2013

  35. [43]

    Balhoff, Susan M

    Nicolas Matentzoglu, James P. Balhoff, Susan M. Bello, Chris Bizon, Matthew Brush, Tiffany J. Callahan, Christopher G. Chute, William D. Duncan, Chris T. Evelo, Davera Gabriel, John Graybeal, Alasdair Gray, Benjamin M. Gyori, Melissa Haendel, Henriette Harmse, Nomi L. Harris, ...

    2022

  36. [44]

    From mathematical modeling and simulation to digital twins: Bridging theory and digital realities in industry and emerging technologies

    Antreas Kantaros, Theodore Ganetsos, Evangelos Pallis, and Michail Papoutsidakis. From mathematical modeling and simulation to digital twins: Bridging theory and digital realities in industry and emerging technologies. Applied Sciences, 15(16):9213, 2025

    2025

  37. [45]

    Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.Journal of Computational Physics, 378:686–707, 2019

    2019

  38. [46]

    Kyriaki Orphanou, Andri Stassopoulou, and Elpida Keravnou. DBN-Extended: A dynamic bayesian network model extended with temporal abstractions for coronary heart disease prognosis.IEEE Journal of Biomedical and Health Informatics, 20(3):944–952, 2016

    2016

  39. [47]

    O’Kane, Dylan Harries, and Mark A

    Terence J. O’Kane, Dylan Harries, and Mark A. Collier. Dynamic bayesian networks for evaluation of granger causal relationships in climate reanalyses.Journal of Advances in Modeling Earth Systems, 13(3):e2020MS002442, 2021

    2021

  40. [48]

    Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof, and Mike Dean

    Ian Horrocks, Peter F. Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof, and Mike Dean. SWRL: A semantic web rule language combining OWL and RuleML. W3C Member Submission, may 2004

    2004

  41. [49]

    Agent-based modeling: Methods and techniques for simulating human systems.Proceedings of the National Academy of Sciences, 99(suppl_3):7280–7287, 2002

    Eric Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems.Proceedings of the National Academy of Sciences, 99(suppl_3):7280–7287, 2002. 35 Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook

    2002

  42. [50]

    Suetsugu, F

    S. Suetsugu, F. Hori, M. Shibata, S. Kitagawa, K. Ishida, T. Asaba, S. Nakazawa, Q. Li, H.-H. Wen, T. Shibauchi, H. Kontani, and Y . Matsuda. Microscopic signatures of an imaginary charge density wave in a kagome metal. Nature Physics, 2026

    2026

  43. [51]

    Goldberg.Genetic Algorithms in Search, Optimization, and Machine Learning

    David E. Goldberg.Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, USA, 1989

    1989

  44. [52]

    Camacho and Carlos Bordons.Model Predictive Control

    Eduardo F. Camacho and Carlos Bordons.Model Predictive Control. Advanced Textbooks in Control and Signal Processing. Springer London, London, UK, 2 edition, 2007

    2007

  45. [53]

    Hakjong Shin, Sang-yeon Lee, Jun-gyu Kim, Dae-Heon Park, Seng-Kyoun Jo, and Younghoon Kwak. Appli- cability evaluation of a temperature humidity index-controlled ventilation system in livestock using a building energy simulation model.Case Studies in Thermal Engineering, 57:10...

    2024

  46. [54]

    Youn, Michael D

    Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, and Zhen Hu. A comprehensive review of digital twin—part 2: Roles of uncertainty quantification and optimization, a battery digital twin, and perspectives.St...

    2022

  47. [55]

    Emulating complex dynamical simulators with random fourier features.SIAM/ASA Journal on Uncertainty Quantification, 12(3):788–811, 2024

    Hossein Mohammadi, Peter Challenor, and Marc Goodfellow. Emulating complex dynamical simulators with random fourier features.SIAM/ASA Journal on Uncertainty Quantification, 12(3):788–811, 2024

    2024

  48. [56]

    Michalis K. Titsias. Variational learning of inducing variables in sparse gaussian processes. InProceedings of the 12th International Conference on Artificial Intelligence and Statistics, volume 5 ofProceedings of Machine Learning Research, pages 567–574. PMLR, 2009

    2009

  49. [57]

    Bayesian pseudocoresets

    Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, and Trevor Campbell. Bayesian pseudocoresets. In Advances in Neural Information Processing Systems, volume 33, pages 14950–14960, 2020

    2020

  50. [58]

    Adaptive learning with gaussian process regression: A compre- hensive review of methods and applications.Machine Learning and Knowledge Extraction, 8(4):101, 2026

    Dominik Polke, Elmar Ahle, and Dirk Söffker. Adaptive learning with gaussian process regression: A compre- hensive review of methods and applications.Machine Learning and Knowledge Extraction, 8(4):101, 2026

    2026

  51. [59]

    Case for a unified surrogate modelling framework in the age of ai.arXiv preprint arXiv:2502.06753, 2025

    Elizaveta Semenova. Case for a unified surrogate modelling framework in the age of ai.arXiv preprint arXiv:2502.06753, 2025

    2025

  52. [60]

    Automatic generation and updating of process industrial digital twins for estimation and control—a review.Frontiers in Control Engineering, 3:954858, 2022

    Wolfgang Birk, Roland Hostettler, Maryam Razi, Khalid Atta, and Rasmus Tammia. Automatic generation and updating of process industrial digital twins for estimation and control—a review.Frontiers in Control Engineering, 3:954858, 2022

    2022

  53. [61]

    Robert Kenley, Navindran Davendralingam, and Daniel DeLaurentis

    Ankur Mour, C. Robert Kenley, Navindran Davendralingam, and Daniel DeLaurentis. Agent-based modeling for systems of systems. InProceedings of the 23rd Annual INCOSE International Symposium. International Council on Systems Engineering, 2013

    2013

  54. [62]

    Mark W. Maier. Architecting principles for systems-of-systems.Systems Engineering, 1(4):267–284, 1998

    1998

  55. [63]

    Understanding transportation as a system-of-systems design problem

    Daniel DeLaurentis. Understanding transportation as a system-of-systems design problem. In43rd AIAA Aerospace Sciences Meeting and Exhibit, page 123, 2005

    2005

  56. [64]

    Architecting digital twins.IEEE Access, 10:50335–50350, 2022

    Enxhi Ferko, Alessio Bucaioni, and Moris Behnam. Architecting digital twins.IEEE Access, 10:50335–50350, 2022

    2022

  57. [65]

    de Vrieze, Rushan Arshad, and Lai Xu

    Paul T. de Vrieze, Rushan Arshad, and Lai Xu. Federated composite manufacturing process simulation using digital twins.International Journal of Simulation and Process Modelling, 21(3):179–191, 2024

    2024

  58. [66]

    Credibility consideration for digital twins in manufacturing.Manufacturing Letters, 35:873–877, 2023

    Guodong Shao, Moneer Helu, Yan Lu, and Thomas White. Credibility consideration for digital twins in manufacturing.Manufacturing Letters, 35:873–877, 2023

    2023

  59. [67]

    Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.npj Digital Medicine, 8(1):156, 2025

    Kaan Sel, Andrea Hawkins-Daarud, Anirban Chaudhuri, Deen Osman, Ahmad Bahai, David Paydarfar, Karen Willcox, Caroline Chung, and Roozbeh Jafari. Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.npj Digit...

    2025

  60. [68]

    Quantifying and combining uncertainty for improving the behavior of digital twin systems

    Julien Deantoni et al. Quantifying and combining uncertainty for improving the behavior of digital twin systems. arXiv preprint arXiv:2402.10535, 2024

    2024 arXiv

  61. [69]

    Model predictive control of heating, ventilation, and air conditioning systems: A state-of-the-art review.Journal of Building Engineering, 60:105067, 2022

    Saeed Taheri, Pouyan Hosseini, and Aida Razban. Model predictive control of heating, ventilation, and air conditioning systems: A state-of-the-art review.Journal of Building Engineering, 60:105067, 2022

    2022

  62. [70]

    van der Linden, E

    A. van der Linden, E. M. de Olde, P. F. Mostert, I. J. M. de Boer, and M. K. van Ittersum. LiGAPS-Beef, a mechanistic model to explore potential and feed-limited beef production 2: Model evaluation and sensitivity analysis.Animal, 13(4):845–855, 2019

    2019

  63. [71]

    R. K. Tabase, G. Naess, and Y . Larring. Ammonia and methane emissions from small herd cattle buildings in a cold climate.Science of the Total Environment, 903:166046, 2023. 36 Trans-Domain Digital Twin: Conceptual Foundations, Architecture, and Research Outlook

    2023

  64. [72]

    Digital twins for advanced manufacturing

    National Institute of Standards and Technology. Digital twins for advanced manufacturing. NIST Program/Project Web Page, 2024

    2024

  65. [73]

    Digital twin system interoperability framework

    Anto Budiardjo and Doug Migliori. Digital twin system interoperability framework. Technical report, Digital Twin Consortium, 2021

    2021

  66. [74]

    International Organization for Standardization, 2021

    International Organization for Standardization.ISO 23247-2:2021: Automation Systems and Integration— Digital Twin Framework for Manufacturing—Part 2: Reference Architecture. International Organization for Standardization, 2021

    2021

  67. [75]

    Correia, Mara Abel, and Karin Becker

    Jaqueline B. Correia, Mara Abel, and Karin Becker. Data management in digital twins: A systematic literature review.Knowledge and Information Systems, 65(8):3165–3196, 2023

    2023

  68. [76]

    Ontologies in digital twins: A systematic literature review.arXiv preprint arXiv:2308.15168, 2023

    Emine Karabulut et al. Ontologies in digital twins: A systematic literature review.arXiv preprint arXiv:2308.15168, 2023

    2023 arXiv

  69. [77]

    Systematic literature review: Digital twins’ role in enhancing security for industry 4.0 systems.Security and Privacy, page e396, 2024

    Mohamad El-Hajj et al. Systematic literature review: Digital twins’ role in enhancing security for industry 4.0 systems.Security and Privacy, page e396, 2024

    2024

  70. [78]

    Privacy and security challenges of the digital twin

    Marija Kuštelega, Renata Mekovec, and Ahmed Shareef. Privacy and security challenges of the digital twin. Journal of Universal Computer Science, 2024

    2024

  71. [79]

    A survey of autonomous vehicle behaviors: Trajectory planning algorithms, sensed collision risks, and user expectations.Sensors, 24(15):4808, 2024

    Taokai Xia and Hui Chen. A survey of autonomous vehicle behaviors: Trajectory planning algorithms, sensed collision risks, and user expectations.Sensors, 24(15):4808, 2024

    2024

  72. [80]

    Model predictive control for autonomous ground vehicles: A review.Autonomous Intelligent Systems, 1(1):4, 2021

    Shuyou Yu, Michael Hirche, Yutao Huang, Hong Chen, and Frank Allgöwer. Model predictive control for autonomous ground vehicles: A review.Autonomous Intelligent Systems, 1(1):4, 2021

    2021

  73. [81]

    A survey of autonomous driving trajectory prediction: Methodologies, challenges, and future prospects.Machines, 13(9):818, 2025

    Miao Xu, Zhi Liu, Bingyi Wang, and Shengyan Li. A survey of autonomous driving trajectory prediction: Methodologies, challenges, and future prospects.Machines, 13(9):818, 2025

    2025

  74. [82]

    Siegel, Isabelle Soerjomataram, and Ahmedin Jemal

    Freddie Bray, Mathieu Laversanne, Hyuna Sung, Jacques Ferlay, Rebecca L. Siegel, Isabelle Soerjomataram, and Ahmedin Jemal. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA: A Cancer Journal for Clinician...

    2022

  75. [83]

    Rockne, Andrea Hawkins-Daarud, Kristin R

    Russell C. Rockne, Andrea Hawkins-Daarud, Kristin R. Swanson, James P. Sluka, James A. Glazier, Paul Macklin, David A. Hormuth, Angela M. Jarrett, Ernesto A. B. F. Lima, J. Tinsley Oden, Thomas E. Yankeelov, et al. The 2019 mathematical oncology roadmap.Physical Biology, 16(4)...

    2019

  76. [84]

    Jarrett, Ernesto A

    Angela M. Jarrett, Ernesto A. B. F. Lima, David A. Hormuth, Michael T. McKenna, Xinzeng Feng, David A. Ekrut, Artur C. M. Resende, Amy Brock, and Thomas E. Yankeelov. Mathematical models of tumor cell proliferation: A review of the literature.Expert Review of Anticancer Therap...

    2018

  77. [85]

    Hormuth, Angela M

    David A. Hormuth, Angela M. Jarrett, Ernesto A. B. F. Lima, Michael T. McKenna, Xiaosong Fu, and Thomas E. Yankeelov. Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathe- matical modeling.Radiation Oncology, 15:4, 2020

    2020

  78. [86]

    Colloquium: Non-markovian dynamics in open quantum systems.Reviews of Modern Physics, 88(2):021002, 2016

    Heinz-Peter Breuer, Elsi-Mari Laine, Jyrki Piilo, and Bassano Vacchini. Colloquium: Non-markovian dynamics in open quantum systems.Reviews of Modern Physics, 88(2):021002, 2016

    2016

  79. [87]

    Frederik Nathan and Mark S. Rudner. Universal lindblad equation for open quantum systems.Physical Review B, 102(11):115109, 2020

    2020

  80. [88]

    Fowler, Matteo Mariantoni, John M

    Austin G. Fowler, Matteo Mariantoni, John M. Martinis, and Andrew N. Cleland. Surface codes: Towards practical large-scale quantum computation.Physical Review A, 86(3):032324, 2012

    2012

  81. [89]

    Quantum error correction below the surface code threshold.Nature, 638:920–926, 2025

    Rajeev Acharya et al. Quantum error correction below the surface code threshold.Nature, 638:920–926, 2025

    2025

  82. [90]

    A game of surface codes: Large-scale quantum computing with lattice surgery.Quantum, 3:128, 2019

    Daniel Litinski. A game of surface codes: Large-scale quantum computing with lattice surgery.Quantum, 3:128, 2019

    2019

  83. [91]

    Morley, Sergey Smolentsev, Alice Ying, Siegfried Malang, Arthur Rowcliffe, and Mike Ulrickson

    Mohamed Abdou, Neil B. Morley, Sergey Smolentsev, Alice Ying, Siegfried Malang, Arthur Rowcliffe, and Mike Ulrickson. Blanket/first wall challenges and required R&D on the pathway to DEMO.Fusion Engineering and Design, 100:2–43, 2015

    2015

  84. [92]

    Taylor, Thomas F

    Chase N. Taylor, Thomas F. Fuerst, Robert J. Pawelko, and Masashi Shimada. The tritium extraction experiment (TEX): A forced convection fusion blanket PbLi loop.Fusion Engineering and Design, 192:113737, 2023

    2023

  85. [93]

    A system dynamics model for stock and flow of tritium in fusion power plant.Fusion Engineering and Design, 98–99:1804–1807, 2015

    Ryuta Kasada, Saerom Kwon, Satoshi Konishi, Yoshiteru Sakamoto, Toshihiko Yamanishi, and Kenji Tobita. A system dynamics model for stock and flow of tritium in fusion power plant.Fusion Engineering and Design, 98–99:1804–1807, 2015. 37 Trans-Domain Digital Twin: Conceptual Fou...

    2015

  86. [94]

    Tamas Bykerk, Sebastian Karl, Mariasole Laureti, Moritz Ertl, and Tobias Ecker. Retro-propulsion in rocket systems: Recent advancements and challenges for the prediction of aerodynamic characteristics and thermal loads.Progress in Aerospace Sciences, 151:101044, 2024

    2024

  87. [95]

    Multidisciplinary design optimization of reusable launch vehicles for different propellants and objectives.Journal of Spacecraft and Rockets, 58(4):977–991, 2021

    Kai Dresia, Simon Jentzsch, Günther Waxenegger-Wilfing, Robson Hahn, Jan Deeken, Michael Oschwald, and Fabio Mota. Multidisciplinary design optimization of reusable launch vehicles for different propellants and objectives.Journal of Spacecraft and Rockets, 58(4):977–991, 2021

    2021

  88. [96]

    Model predictive control for reusable space launcher guidance improvement.Acta Astronautica, 193:767–778, 2022

    Jacopo Guadagnini, Michèle Lavagna, and Paulo Rosa. Model predictive control for reusable space launcher guidance improvement.Acta Astronautica, 193:767–778, 2022

    2022

  89. [97]

    Shiva Harirchi, Steven Wainaina, Taner Sar, Seyed Ali Nojoumi, Mohammad Parchami, Mehran Parchami, Sunita Varjani, Samir Kumar Khanal, Jonathan W. C. Wong, Mukesh Kumar Awasthi, and Mohammad J. Taherzadeh. Microbiological insights into anaerobic digestion for biogas, hydrogen ...

    2022

  90. [98]

    Batstone, Jürg Keller, Irini Angelidaki, Sergey V

    Damien J. Batstone, Jürg Keller, Irini Angelidaki, Sergey V . Kalyuzhnyi, Spyros G. Pavlostathis, Alberto Rozzi, Willy T. M. Sanders, Hans Siegrist, and Vasily A. Vavilin. The IW A anaerobic digestion model no. 1 (ADM1). Water Science and Technology, 45(10):65–73, 2002

    2002

  91. [99]

    Dynamical model development and parameter identification for an anaerobic wastewater treatment process.Biotechnology and Bioengineering, 75(4):424–438, 2001

    Olivier Bernard, Zahia Hadj-Sadok, Denis Dochain, Antoine Genovesi, and Jean-Philippe Steyer. Dynamical model development and parameter identification for an anaerobic wastewater treatment process.Biotechnology and Bioengineering, 75(4):424–438, 2001

    2001

  92. [100]

    Dudley, Zhiyong Jason Ren, and David M

    Harry J. Dudley, Zhiyong Jason Ren, and David M. Bortz. Competitive exclusion in a DAE model for microbial electrolysis cells.Mathematical Biosciences and Engineering, 17(6):6637–6656, 2020

    2020

  93. [101]

    Rakesh Kumar, Lal Singh, and A. W. Zularisam. Microbial fuel cells: A comprehensive review for beginners.3 Biotech, 12:9, 2022

    2022

  94. [102]

    Model development of bioelectrochemical systems: A review.Water Research, 229:119456, 2023

    Zhen Li et al. Model development of bioelectrochemical systems: A review.Water Research, 229:119456, 2023

    2023

  95. [103]

    Hubertus V . M. Hamelers, Annemiek Ter Heijne, Nicole Stein, René A. Rozendal, and Cees J. N. Buisman. Butler– volmer–monod model for describing bio-anode polarization curves.Bioresource Technology, 102(1):381–387, 2011

    2011

  96. [104]

    Rahim, Golam Miah, and Magaji G

    Yusuff Oladosu, Mohd Yusop Rafii, Norhani Abdullah, Ghazali Hussin, Abdul Ramli, Harun A. Rahim, Golam Miah, and Magaji G. Usman. Principle and application of plant mutagenesis in crop improvement: A review. Biotechnology & Biotechnological Equipment, 30(1):1–16, 2016

    2016

  97. [105]

    Holme, Per L

    Inger B. Holme, Per L. Gregersen, and Henrik Brinch-Pedersen. Induced genetic variation in crop plants by random or targeted mutagenesis: Convergence and differences.Frontiers in Plant Science, 10:1468, 2019

    2019

  98. [106]

    From classical radiation to modern radiation: Past, present, and future of radiation mutation breeding.Frontiers in Public Health, 9:768071, 2021

    Lei Ma, Fang Kong, Kai Sun, Tian Wang, and Tian Guo. From classical radiation to modern radiation: Past, present, and future of radiation mutation breeding.Frontiers in Public Health, 9:768071, 2021

    2021

  99. [107]

    Hatilima, Hubert Roth, and Vadim Zhmud

    Nasser Gyagenda, Jasper V . Hatilima, Hubert Roth, and Vadim Zhmud. A review of GNSS-independent UA V navigation techniques.Robotics and Autonomous Systems, 152:104069, 2022

    2022

  100. [108]

    UFOMap: An efficient probabilistic 3D mapping framework that embraces the unknown.IEEE Robotics and Automation Letters, 5(4):6411–6418, 2020

    Daniel Duberg and Patric Jensfelt. UFOMap: An efficient probabilistic 3D mapping framework that embraces the unknown.IEEE Robotics and Automation Letters, 5(4):6411–6418, 2020

    2020

  101. [109]

    V oxblox: Incremental 3D euclidean signed distance fields for on-board MA V planning

    Helen Oleynikova, Zachary Taylor, Marius Fehr, Juan Nieto, and Roland Siegwart. V oxblox: Incremental 3D euclidean signed distance fields for on-board MA V planning. In2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1366–1373. IEEE, 2017

    2017

  102. [110]

    Systematic review of digital twin technology and applications.Visual Computing for Industry, Biomedicine, and Art, 6(1):10, 2023

    Jun-Feng Yao, Yong Yang, Xiao-Chang Wang, and Xiao-Ping Zhang. Systematic review of digital twin technology and applications.Visual Computing for Industry, Biomedicine, and Art, 6(1):10, 2023

    2023

  103. [111]

    Botín-Sanabria, Adriana-Simona Mihaita, Rodrigo E

    Diego M. Botín-Sanabria, Adriana-Simona Mihaita, Rodrigo E. Peimbert-García, Miguel A. Ramírez-Moreno, Ricardo A. Ramírez-Mendoza, and Jorge de J. Lozoya-Santos. Digital twin technology challenges and applica- tions: A comprehensive review.Remote Sensing, 14(6):1335, 2022

    2022

  104. [112]

    Intelligent digital twin (iDT) for supply chain stress-testing, resilience and viability.International Journal of Production Economics, 263:108938, 2023

    Dmitry Ivanov. Intelligent digital twin (iDT) for supply chain stress-testing, resilience and viability.International Journal of Production Economics, 263:108938, 2023

    2023

  105. [113]

    Digital twin conceptual framework for improving critical infrastructure resilience.Automatisierungstechnik, 69(12):1062– 1080, 2021

    Eva Brucherseifer, Heiko Winter, Andreas Mentges, Martina Mühlhäuser, and Marco Hellmann. Digital twin conceptual framework for improving critical infrastructure resilience.Automatisierungstechnik, 69(12):1062– 1080, 2021

    2021

  106. [114]

    Realising the digital twin: A thematic review and analysis of the ethical, legal, and social issues for digital twins in healthcare.AI & Society, 41(5):5243–5267, 2026

    Christopher David Burr, Shuang Qian, Peter Winter, Tim Chico, Camila Rangel Smith, David Wagg, and Steven Alexander Niederer. Realising the digital twin: A thematic review and analysis of the ethical, legal, and social issues for digital twins in healthcare.AI & Society, 41(5)...

    2026

  107. [115]

    Ethical issues of digital twins for personalized health care service: Preliminary mapping study.Journal of Medical Internet Research, 24(1):e33081, 2022

    Pei-Hua Huang, Ki-Hun Kim, and Maartje Schermer. Ethical issues of digital twins for personalized health care service: Preliminary mapping study.Journal of Medical Internet Research, 24(1):e33081, 2022. 39

    2022

  108. [2024]

    Association for Computing Machinery

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.