Paper Citation Record · LEDGER

Meta-Learning for Physically-Constrained Neural System Identification

As of 11 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2501.06167.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.06167 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

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measured 60 of 60 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T06:00:50.280935Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T06:01:08.391196Z

Reference resolution

59 of 59 outbound references displayed

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External citation measurements

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Outbound references

Observation 821f6fa0-e234-49c3-afd7-28bafa617d64 · outbound

This paper cites State-space neural network. Properties and application,.

Meta-Learning for Physically-Constrained Neural System Identification State-space neural network. Properties and application,

Reference 1

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Observation d30a60c2-42fd-4023-868e-545f5f084c41 · outbound

This paper cites Identification of state-space linear parameter-varying models using artificial neural networks,.

Meta-Learning for Physically-Constrained Neural System Identification Identification of state-space linear parameter-varying models using artificial neural networks,

Reference 2

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Observation 02b76dcf-9d3d-4001-b7dd-251a64784835 · outbound

This paper cites Learning neural state-space models: do we need a state estimator?.

Meta-Learning for Physically-Constrained Neural System Identification Learning neural state-space models: do we need a state estimator?

Reference 3

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Observation 04526a7b-f215-495b-be6a-de73a404bf48 · outbound

This paper cites Constructing Neural Network-Based Models for Simulating Dynamical Systems.

Meta-Learning for Physically-Constrained Neural System Identification Constructing Neural Network-Based Models for Simulating Dynamical Systems

Reference 4

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Observation e3f96500-9b17-4143-b27f-fa19cf6ec179 · outbound

This paper cites Model structures and fitting criteria for system identification with neural networks,.

Meta-Learning for Physically-Constrained Neural System Identification Model structures and fitting criteria for system identification with neural networks,

Reference 5

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This paper cites dynoNet: A neural network architecture for learning dynamical systems,.

Meta-Learning for Physically-Constrained Neural System Identification dynoNet: A neural network architecture for learning dynamical systems,

Reference 6

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Observation 4b04f144-805c-454c-8556-cd3f5ff62b0a · outbound

This paper cites Automating discovery of physics-informed neural state space models via learning and evolution,.

Meta-Learning for Physically-Constrained Neural System Identification Automating discovery of physics-informed neural state space models via learning and evolution,

Reference 7

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Observation 13e64c07-0956-48c3-968d-7a4d330e6d92 · outbound

This paper cites Learning nonlinear state–space models using autoencoders,.

Meta-Learning for Physically-Constrained Neural System Identification Learning nonlinear state–space models using autoencoders,

Reference 8

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Observation b449e35b-e69d-43f2-a6d7-b30584a409e7 · outbound

This paper cites Deep identification of nonlinear systems in Koopman form,.

Meta-Learning for Physically-Constrained Neural System Identification Deep identification of nonlinear systems in Koopman form,

Reference 9

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Observation 221a9fac-e5fe-4b9c-91c9-b5c2789f1aec · outbound

This paper cites On learning Hamiltonian systems from data,.

Meta-Learning for Physically-Constrained Neural System Identification On learning Hamiltonian systems from data,

Reference 10

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Observation 11192e6b-745e-44c4-8a92-387fb90f97da · outbound

This paper cites Nonlinear state-space identification using deep encoder networks,.

Meta-Learning for Physically-Constrained Neural System Identification Nonlinear state-space identification using deep encoder networks,

Reference 11

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Observation 1606ff4f-ba14-4772-acb2-69685befcf47 · outbound

This paper cites Dynamical systems of continuous spectra,.

Meta-Learning for Physically-Constrained Neural System Identification Dynamical systems of continuous spectra,

Reference 12

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Observation ae99460b-c315-404e-85dd-0ceee2cc49bd · outbound

This paper cites Deep learning for universal linear embeddings of nonlinear dynamics,.

Meta-Learning for Physically-Constrained Neural System Identification Deep learning for universal linear embeddings of nonlinear dynamics,

Reference 13

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Observation 28ac533a-a780-4193-9e14-648bcdbc8f6a · outbound

This paper cites Identifying the dynamics of a system by leveraging data from similar systems,.

Meta-Learning for Physically-Constrained Neural System Identification Identifying the dynamics of a system by leveraging data from similar systems,

Reference 14

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Observation 161526cd-40fe-4cd1-ae07-8188763a1e40 · outbound

This paper cites From system models to class models: An in-context learning paradigm,.

Meta-Learning for Physically-Constrained Neural System Identification From system models to class models: An in-context learning paradigm,

Reference 15

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Observation 651402a5-ca9e-4f92-aa41-1f0e2fe806ad · outbound

This paper cites On the adaptation of in-context learners for system identification,.

Meta-Learning for Physically-Constrained Neural System Identification On the adaptation of in-context learners for system identification,

Reference 16

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Observation fd4e7191-1e32-415b-8b6b-621dde7d5d1e · outbound

This paper cites An L-BFGS-B approach for linear and nonlinear system identification under $\ell_1$ and group-Lasso regularization.

Meta-Learning for Physically-Constrained Neural System Identification An L-BFGS-B approach for linear and nonlinear system identification under $\ell_1$ and group-Lasso regularization

Reference 17

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Observation cdeb01fa-691c-44a6-84c6-f8894c4c7e0d · outbound

This paper cites Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization,.

Meta-Learning for Physically-Constrained Neural System Identification Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization,

Reference 18

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Observation 28509859-80d3-455f-8d55-5f3afe5c9b1c · outbound

This paper cites Optimizing closed-loop performance with data from similar systems: A Bayesian meta-learning approach,.

Meta-Learning for Physically-Constrained Neural System Identification Optimizing closed-loop performance with data from similar systems: A Bayesian meta-learning approach,

Reference 19

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Observation 0ee288cf-a568-4142-86aa-60f647d26c34 · outbound

This paper cites Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems.

Meta-Learning for Physically-Constrained Neural System Identification Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems

Reference 20

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Observation 9fd5fac8-b642-44af-bb0b-9a7026c9e0b5 · outbound

This paper cites Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations.

Meta-Learning for Physically-Constrained Neural System Identification Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations

Reference 21

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Observation 7c678edd-ae9f-4bc9-832f-b90759414470 · outbound

This paper cites Meta-Learning Guarantees for Online Receding Horizon Learning Control.

Meta-Learning for Physically-Constrained Neural System Identification Meta-Learning Guarantees for Online Receding Horizon Learning Control

Reference 22

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Meta-Learning for Physically-Constrained Neural System Identification MPC of uncertain nonlinear systems with meta-learning for fast adaptation of neural predictive models,

Reference 23

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Observation 0be88a4c-b4db-4df1-8114-d2e31a4437c6 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Meta-Learning for Physically-Constrained Neural System Identification Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 24

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This paper cites How to train your MAML,.

Meta-Learning for Physically-Constrained Neural System Identification How to train your MAML,

Reference 25

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Observation c22413fe-55cc-465e-9e26-097fc75fcdd7 · outbound

This paper cites Rapid learning or feature reuse? Towards understanding the effectiveness of MAML,.

Meta-Learning for Physically-Constrained Neural System Identification Rapid learning or feature reuse? Towards understanding the effectiveness of MAML,

Reference 26

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Observation 2de5896d-fd74-4d8a-98dc-89dc6ed09a16 · outbound

This paper cites Meta-learning with implicit gradients,.

Meta-Learning for Physically-Constrained Neural System Identification Meta-learning with implicit gradients,

Reference 27

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Observation eed598c2-36f9-41ee-b4b9-b53bb97745a3 · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Meta-Learning for Physically-Constrained Neural System Identification On First-Order Meta-Learning Algorithms

Reference 28

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This paper cites A New First-Order Meta-Learning Algorithm with Convergence Guarantees.

Meta-Learning for Physically-Constrained Neural System Identification A New First-Order Meta-Learning Algorithm with Convergence Guarantees

Reference 29

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Observation b8a0680f-221b-4e3b-aa14-b11daa8c86a8 · outbound

This paper cites Mauroy, Y.

Meta-Learning for Physically-Constrained Neural System Identification Mauroy, Y

Reference 30

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Meta-Learning for Physically-Constrained Neural System Identification Unresolved cited work

Reference 31

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This paper cites Double description method revisited,.

Meta-Learning for Physically-Constrained Neural System Identification Double description method revisited,

Reference 32

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Observation b847cb80-a422-4440-ba77-8360a0365955 · outbound

This paper cites Constrained smoothers for state estimation of vapor compression cycles,.

Meta-Learning for Physically-Constrained Neural System Identification Constrained smoothers for state estimation of vapor compression cycles,

Reference 33

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Observation 1c753223-9698-46eb-9d68-635612103d1d · outbound

This paper cites Constrained Kalman filtering via density function truncation for turbofan engine health estimation,.

Meta-Learning for Physically-Constrained Neural System Identification Constrained Kalman filtering via density function truncation for turbofan engine health estimation,

Reference 34

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Observation eef5c9a2-735a-474a-a1d7-32da6a6bf8cb · outbound

This paper cites Constrained State Estimation -- A Review.

Meta-Learning for Physically-Constrained Neural System Identification Constrained State Estimation -- A Review

Reference 35

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Meta-Learning for Physically-Constrained Neural System Identification Multi-pass extended kalman smoother with partially-known constraints for estimation of vapor compression cycles,

Reference 36

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Meta-Learning for Physically-Constrained Neural System Identification Modeling magnetic fields using Gaussian processes,

Reference 37

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Observation 5d1ce576-4a7f-4c47-a4a6-2cac05ed5ccb · outbound

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Meta-Learning for Physically-Constrained Neural System Identification Linearly Constrained Neural Networks

Reference 38

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Meta-Learning for Physically-Constrained Neural System Identification Unresolved cited work

Reference 39

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Meta-Learning for Physically-Constrained Neural System Identification Searching for Activation Functions

Reference 40

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This paper cites Hysteretic benchmark with a dynamic nonlinearity,.

Meta-Learning for Physically-Constrained Neural System Identification Hysteretic benchmark with a dynamic nonlinearity,

Reference 41

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This paper cites On the initialization of nonlinear lfr model identification with the best linear approximation,.

Meta-Learning for Physically-Constrained Neural System Identification On the initialization of nonlinear lfr model identification with the best linear approximation,

Reference 42

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Observation 9bedeba2-058c-4fc5-bf92-e72f0a44f3da · outbound

This paper cites dynoNet: A neural network architecture for learning dynamical systems,.

Meta-Learning for Physically-Constrained Neural System Identification dynoNet: A neural network architecture for learning dynamical systems,

Reference 43

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This paper cites Polynomial state-space model decoupling for the identification of hysteretic systems,.

Meta-Learning for Physically-Constrained Neural System Identification Polynomial state-space model decoupling for the identification of hysteretic systems,

Reference 44

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Observation 5592d1ac-f761-49b8-a99d-e3b29afbd71e · outbound

This paper cites Automatic modeling with local model networks for benchmark processes,.

Meta-Learning for Physically-Constrained Neural System Identification Automatic modeling with local model networks for benchmark processes,

Reference 45

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Observation 41c0d959-60f8-4565-9323-c4b6c074b89d · outbound

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Meta-Learning for Physically-Constrained Neural System Identification PyTorch: An imperative style, high-performance deep learning library,

Reference 46

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Observation 9513bbc5-c34a-48ef-9f12-d13fa1320ae2 · outbound

This paper cites How transferable are features in deep neural networks?.

Meta-Learning for Physically-Constrained Neural System Identification How transferable are features in deep neural networks?

Reference 47

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Observation 0886c72b-c0e7-4671-942f-cc5e9c6fe37d · outbound

This paper cites Digital twin design with on-line calibration for HV AC systems in buildings,.

Meta-Learning for Physically-Constrained Neural System Identification Digital twin design with on-line calibration for HV AC systems in buildings,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:57.645324Z

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Observation 3bbd828d-c51d-44f0-a87b-5e70aff88b23 · outbound

This paper cites Digital Twins of Vapor Compression Cycles: Challenges and Opportunities,.

Meta-Learning for Physically-Constrained Neural System Identification Digital Twins of Vapor Compression Cycles: Challenges and Opportunities,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:57.484752Z

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Observation 1d5771fe-f978-4e87-a8c0-4efb003c1db3 · outbound

This paper cites 𝐻∞ loop-shaped model predictive control with heat pump application,.

Meta-Learning for Physically-Constrained Neural System Identification 𝐻∞ loop-shaped model predictive control with heat pump application,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:57.335186Z

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Observation a4c3ffc0-2a4d-4585-8966-3c1019e06b9a · outbound

This paper cites Scalable Bayesian optimization for model calibration: Case study on coupled building and HV AC dynamics,.

Meta-Learning for Physically-Constrained Neural System Identification Scalable Bayesian optimization for model calibration: Case study on coupled building and HV AC dynamics,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:57.191686Z

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Observation 1eb6e9ab-23a1-421f-ad87-2a2fd65c1fb1 · outbound

This paper cites Controller tuning by Bayesian optimization an application to a heat pump,.

Meta-Learning for Physically-Constrained Neural System Identification Controller tuning by Bayesian optimization an application to a heat pump,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:57.067526Z

Source-reported events for the cited work

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Observation 13b0faec-a61e-4d49-992a-a71e053ef3cd · outbound

This paper cites Accelerating self-optimization control of refrigerant cycles with bayesian optimization and adaptive moment estimation,.

Meta-Learning for Physically-Constrained Neural System Identification Accelerating self-optimization control of refrigerant cycles with bayesian optimization and adaptive moment estimation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:56.926917Z

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Observation cf1571de-760b-4b53-8831-d2ae59f36577 · outbound

This paper cites Transient modeling of a flash tank vapor injection heat pump system–Part I: Model development,.

Meta-Learning for Physically-Constrained Neural System Identification Transient modeling of a flash tank vapor injection heat pump system–Part I: Model development,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:56.784754Z

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Observation 3c1c6ef1-4c68-4274-b0b4-5c5b767c3e65 · outbound

This paper cites Learning residual dynamics via physics-augmented neural networks: Application to vapor compression cycles,.

Meta-Learning for Physically-Constrained Neural System Identification Learning residual dynamics via physics-augmented neural networks: Application to vapor compression cycles,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:56.644755Z

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Observation 145b8368-c895-4d0d-8e90-458f6833b023 · outbound

This paper cites Physics-constrained deep autoencoded kalman filters for estimating vapor compression system states,.

Meta-Learning for Physically-Constrained Neural System Identification Physics-constrained deep autoencoded kalman filters for estimating vapor compression system states,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:56.454743Z

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Observation a2f95a32-0f6b-4d5e-a462-afb682746817 · outbound

This paper cites Constrained gaussian-process state-space models for online magnetic-field estimation,.

Meta-Learning for Physically-Constrained Neural System Identification Constrained gaussian-process state-space models for online magnetic-field estimation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:56.314758Z

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Observation 2568cd6a-41a8-416f-89a9-d7e29b93fde0 · outbound

This paper cites How feasible is the use of magnetic field alone for indoor positioning?.

Meta-Learning for Physically-Constrained Neural System Identification How feasible is the use of magnetic field alone for indoor positioning?

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:56.190864Z

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Observation 87afae29-178c-4332-9b16-867053f10226 · outbound

This paper cites Characterization of the indoor magnetic field for applications in localization and mapping,.

Meta-Learning for Physically-Constrained Neural System Identification Characterization of the indoor magnetic field for applications in localization and mapping,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:11:56.004912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation ba9f78d7-db9a-4bbe-831c-bc9696e1040f · inbound

Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems cites this paper.

Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems Meta-Learning for Physically-Constrained Neural System Identification

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:01:08.394795Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.