Paper Citation Record · LEDGER

Safe Physics-Informed Machine Learning for Dynamics and Control

As of 19 August 2026, this Paper Citation Record lists 100 of 198 outbound references and 4 inbound Pith citation observations for arXiv:2504.12952.

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

pith.paper-citation-record.v1
2504.12952 v2

Coverage vector

measured 100 of 198 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:22:02.918536Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:42:34.194243Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:07:48.116161Z

Reference resolution

100 of 198 outbound references displayed

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Outbound references

Observation 7bea58c0-fe7d-4d0d-ae62-7ed9c529ea38 · outbound

This paper cites Physics-informed machine learning for modeling and control of dynamical systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Physics-informed machine learning for modeling and control of dynamical systems,

Reference 1

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Observation 55075eb0-350a-46b1-baf8-d8c23250cb69 · outbound

This paper cites Physics-informed machine learning,.

Safe Physics-Informed Machine Learning for Dynamics and Control Physics-informed machine learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.541791Z

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Observation 0137b08c-12e4-43ce-ae6d-b28496665e81 · outbound

This paper cites Scientific machine learning benchmarks,.

Safe Physics-Informed Machine Learning for Dynamics and Control Scientific machine learning benchmarks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.545906Z

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Observation 51c0f78f-d246-40d9-8826-6a9dbde1c1d4 · outbound

This paper cites Safe control with learned certificates: A survey of neural Lyapunov, barrier, and contraction methods for robotics and control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Safe control with learned certificates: A survey of neural Lyapunov, barrier, and contraction methods for robotics and control,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.549922Z

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Observation 16bd2834-6cac-4433-ab0d-abf8be6669ff · outbound

This paper cites Safe learning in robotics: From learning-based control to safe reinforcement learning,.

Safe Physics-Informed Machine Learning for Dynamics and Control Safe learning in robotics: From learning-based control to safe reinforcement learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.553687Z

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Observation 126c5120-1239-4917-b467-13239ddf4f73 · outbound

This paper cites Safe control against uncertainty: A compre- hensive review of control barrier function strategies,.

Safe Physics-Informed Machine Learning for Dynamics and Control Safe control against uncertainty: A compre- hensive review of control barrier function strategies,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.557587Z

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Observation fe48dc5e-39d9-4f29-a92f-5e4d68f0e0aa · outbound

This paper cites Data-driven safety filters: Hamilton- jacobi reachability, control barrier functions, and predictive methods for uncertain systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Data-driven safety filters: Hamilton- jacobi reachability, control barrier functions, and predictive methods for uncertain systems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.561594Z

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Observation 5d399a31-d30c-4027-89ee-680d1ebe07dc · outbound

This paper cites On dynamic mode decomposition: Theory and applications,.

Safe Physics-Informed Machine Learning for Dynamics and Control On dynamic mode decomposition: Theory and applications,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.565190Z

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Observation 5bfc81f3-57fc-47f7-b26b-c74beae208a0 · outbound

This paper cites Applied koopmanism,.

Safe Physics-Informed Machine Learning for Dynamics and Control Applied koopmanism,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.568831Z

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Observation 36437ff7-cf53-4263-a5b2-86491a02716f · outbound

This paper cites Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.572709Z

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Observation 55f33fca-85d5-41fa-9b0c-b2ed228d123b · outbound

This paper cites Koopman operator, geometry, and learning,.

Safe Physics-Informed Machine Learning for Dynamics and Control Koopman operator, geometry, and learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.576299Z

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Observation 2f107241-3842-429a-a02d-da7421ebe40b · outbound

This paper cites Modern koopman theory for dynamical systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Modern koopman theory for dynamical systems,

Reference 12

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Observation 73fd8fa4-5f8e-4549-8237-3f0ca4741f27 · outbound

This paper cites an unresolved cited work.

Safe Physics-Informed Machine Learning for Dynamics and Control Unresolved cited work

Reference 13

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Observation 4f398396-0051-4a2f-8caa-cfe123151df4 · outbound

This paper cites Neural ordinary differential equations,.

Safe Physics-Informed Machine Learning for Dynamics and Control Neural ordinary differential equations,

Reference 14

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Observation 9df11ad9-7a87-43c9-bfb6-01ba95c981a0 · outbound

This paper cites Zero-shot transfer of neural odes,.

Safe Physics-Informed Machine Learning for Dynamics and Control Zero-shot transfer of neural odes,

Reference 15

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Observation caab80e2-b3ee-444f-8856-032cf9e19ddf · outbound

This paper cites Learning differential equations that are easy to solve,.

Safe Physics-Informed Machine Learning for Dynamics and Control Learning differential equations that are easy to solve,

Reference 16

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Observation cd5bac96-778d-4f22-9234-70c29a150b0b · outbound

This paper cites Neural networks with physics-informed architectures and constraints for dynamical systems modeling,.

Safe Physics-Informed Machine Learning for Dynamics and Control Neural networks with physics-informed architectures and constraints for dynamical systems modeling,

Reference 17

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Observation c241892a-6871-4c31-b421-268350b7ec66 · outbound

This paper cites Constructing neural network based models for simulating dynamical systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Constructing neural network based models for simulating dynamical systems,

Reference 18

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Observation d4cb3b46-2a42-4325-a17b-9673e0126aff · outbound

This paper cites Hierarchical deep learning of multiscale differential equation time-steppers,.

Safe Physics-Informed Machine Learning for Dynamics and Control Hierarchical deep learning of multiscale differential equation time-steppers,

Reference 19

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Observation 6b8f9c9e-268f-40a3-ac10-b6c98174fa62 · outbound

This paper cites Gedon, N.

Safe Physics-Informed Machine Learning for Dynamics and Control Gedon, N

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.610250Z

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Observation e55ad8a7-2793-4ac0-81e1-bf0be0d531aa · outbound

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

Safe Physics-Informed Machine Learning for Dynamics and Control Learning nonlinear state-space models using deep autoencoders,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.613974Z

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Observation 0414b913-306d-4d8b-af55-734be783c622 · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.617613Z

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Observation 1940819c-1567-4e12-b780-25e32dc50457 · outbound

This paper cites Constrained sparse Galerkin regression,.

Safe Physics-Informed Machine Learning for Dynamics and Control Constrained sparse Galerkin regression,

Reference 23

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Observation 19db07cc-78e3-49e7-b59b-4cbd42db6dac · outbound

This paper cites Promoting global stability in data-driven models of quadratic nonlinear dynamics,.

Safe Physics-Informed Machine Learning for Dynamics and Control Promoting global stability in data-driven models of quadratic nonlinear dynamics,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.624872Z

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Observation 95cdaecf-6c52-46ef-8771-6881ced1df10 · outbound

This paper cites On long-term boundedness of galerkin models,.

Safe Physics-Informed Machine Learning for Dynamics and Control On long-term boundedness of galerkin models,

Reference 25

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Observation f9ac5ed0-9209-4da9-b353-e27cf7d3e7df · outbound

This paper cites Variable projection methods for an optimized dynamic mode decomposition,.

Safe Physics-Informed Machine Learning for Dynamics and Control Variable projection methods for an optimized dynamic mode decomposition,

Reference 26

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Observation f5b8696d-f3df-4668-b461-3e74324a5a97 · outbound

This paper cites Physics-informed dynamic mode decomposition,.

Safe Physics-Informed Machine Learning for Dynamics and Control Physics-informed dynamic mode decomposition,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.635730Z

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Observation 4108fd5a-fc7e-4f42-9ad8-5eacb9021943 · outbound

This paper cites Ergodic theory, dynamic mode decomposi- tion, and computation of spectral properties of the koopman operator,.

Safe Physics-Informed Machine Learning for Dynamics and Control Ergodic theory, dynamic mode decomposi- tion, and computation of spectral properties of the koopman operator,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.639303Z

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Observation b5a05d4e-5cae-4a5d-93ef-98f66eeb4eae · outbound

This paper cites Data-driven spectral analysis of the koopman operator,.

Safe Physics-Informed Machine Learning for Dynamics and Control Data-driven spectral analysis of the koopman operator,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.642723Z

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Observation dd60bbc8-0da7-4b32-9b06-d5d0fbb09711 · outbound

This paper cites Global stability analysis using the eigenfunctions of the koopman operator,.

Safe Physics-Informed Machine Learning for Dynamics and Control Global stability analysis using the eigenfunctions of the koopman operator,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.646126Z

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Observation 46f6c4f5-8400-4761-9ad2-ecacea4baaae · outbound

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

Safe Physics-Informed Machine Learning for Dynamics and Control Deep learning for universal linear embeddings of nonlinear dynamics,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.649941Z

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Observation 97edcaae-4cc2-400c-b735-d6a257aac237 · outbound

This paper cites Extended dynamic mode decomposition with learned koopman eigenfunctions for prediction and control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Extended dynamic mode decomposition with learned koopman eigenfunctions for prediction and control,

Reference 32

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Observation d3f1d759-149d-4d61-9368-04a158a20c17 · outbound

This paper cites Learning deep neural network representations for Koopman operators of nonlinear dynamical systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Learning deep neural network representations for Koopman operators of nonlinear dynamical systems,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.656942Z

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Observation 3fb768e4-b16f-4578-997c-dfdb704ad0f8 · outbound

This paper cites Learning Koopman invariant subspaces for dynamic mode decomposition,.

Safe Physics-Informed Machine Learning for Dynamics and Control Learning Koopman invariant subspaces for dynamic mode decomposition,

Reference 34

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Observation 30578f54-95fc-41e3-bdb2-f39df53f6e47 · outbound

This paper cites Deep Koopman operator with control for nonlinear systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Deep Koopman operator with control for nonlinear systems,

Reference 35

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Observation 8c7945ce-fe5f-4393-96a5-c625b90ea43e · outbound

This paper cites Deep learning of Koopman representation for control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Deep learning of Koopman representation for control,

Reference 36

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Observation 7bf498a9-2b3d-4fbc-98d7-6961526577f3 · outbound

This paper cites Data-driven discovery of Koopman eigenfunctions for control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Data-driven discovery of Koopman eigenfunctions for control,

Reference 37

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Observation 2e0a665c-2363-47b2-aa3c-e8c08d57fa0d · outbound

This paper cites DeSKO: Stability- assured robust control with a deep stochastic Koopman operator,.

Safe Physics-Informed Machine Learning for Dynamics and Control DeSKO: Stability- assured robust control with a deep stochastic Koopman operator,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.675066Z

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Observation 301c4966-578c-439e-8887-7169ec59d3de · outbound

This paper cites Learning stable models for prediction and control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Learning stable models for prediction and control,

Reference 39

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Observation 7a60ff45-a35d-429f-802c-1c9bb1b14bdd · outbound

This paper cites Diffeomorphically learning stable Koopman operators,.

Safe Physics-Informed Machine Learning for Dynamics and Control Diffeomorphically learning stable Koopman operators,

Reference 40

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Observation df0129aa-aa10-4abe-be33-48fc0f830a2e · outbound

This paper cites Dissipative deep neural dynamical systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Dissipative deep neural dynamical systems,

Reference 41

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Observation c8d1904b-6d35-4487-8033-537be25df3ad · outbound

This paper cites Constrained block nonlinear neural dynamical models,.

Safe Physics-Informed Machine Learning for Dynamics and Control Constrained block nonlinear neural dynamical models,

Reference 42

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This paper cites On the stochastic stability of deep markov models,.

Safe Physics-Informed Machine Learning for Dynamics and Control On the stochastic stability of deep markov models,

Reference 43

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Observation 9f363133-6855-416c-af20-de5ba524c1d2 · outbound

This paper cites Stabilizing gradients for deep neural networks via efficient svd parameterization,.

Safe Physics-Informed Machine Learning for Dynamics and Control Stabilizing gradients for deep neural networks via efficient svd parameterization,

Reference 44

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Observation 08264e95-ad8c-4276-b014-7066f8dfd253 · outbound

This paper cites NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations,.

Safe Physics-Informed Machine Learning for Dynamics and Control NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations,

Reference 45

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Observation 39e08184-a5ab-413e-8ae3-fd3cda9e8fc8 · outbound

This paper cites Port- Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Port- Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control,

Reference 46

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Observation 6f71937d-ce63-471d-96f5-d4774b4c1c3d · outbound

This paper cites A port-Hamiltonian approach to power network modeling and analysis,.

Safe Physics-Informed Machine Learning for Dynamics and Control A port-Hamiltonian approach to power network modeling and analysis,

Reference 47

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Observation 195fb28e-2ce9-4958-824c-abe9e1e5b01d · outbound

This paper cites Symplectic Gaussian process regression of maps in Hamiltonian systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Symplectic Gaussian process regression of maps in Hamiltonian systems,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.711597Z

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Observation c4d1587e-3388-48d7-bc8e-94b8e3b728b9 · outbound

This paper cites Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks,.

Safe Physics-Informed Machine Learning for Dynamics and Control Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.715214Z

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Observation 42137121-297a-4219-b47f-52bf59765fe6 · outbound

This paper cites Data-driven identification of latent port-Hamiltonian systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control Data-driven identification of latent port-Hamiltonian systems,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.719034Z

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Observation 927c1236-2718-432c-acef-9fae2645b4dd · outbound

This paper cites Data-driven model reduction for port-Hamiltonian and network systems in the Loewner framework,.

Safe Physics-Informed Machine Learning for Dynamics and Control Data-driven model reduction for port-Hamiltonian and network systems in the Loewner framework,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.722631Z

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Observation 52e9f89b-779c-4354-ad7b-738dd97f8f03 · outbound

This paper cites Port-Hamiltonian Systems Theory: An Introductory Overview,.

Safe Physics-Informed Machine Learning for Dynamics and Control Port-Hamiltonian Systems Theory: An Introductory Overview,

Reference 52

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Observation d344838b-7295-495e-a6cd-6209047d98da · outbound

This paper cites Automatic differen- tiation in pytorch,.

Safe Physics-Informed Machine Learning for Dynamics and Control Automatic differen- tiation in pytorch,

Reference 53

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Observation 9c6b704b-7df4-4dd6-94d8-ffd33e3c878c · outbound

This paper cites Automatic Differentiation in Machine Learning: A Survey,.

Safe Physics-Informed Machine Learning for Dynamics and Control Automatic Differentiation in Machine Learning: A Survey,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.734672Z

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Observation 920a32cc-ba2d-4eac-a869-7e6deac31478 · outbound

This paper cites Data-Driven Reduced-Order Models for Port-Hamiltonian Systems with Operator Inference,.

Safe Physics-Informed Machine Learning for Dynamics and Control Data-Driven Reduced-Order Models for Port-Hamiltonian Systems with Operator Inference,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.738749Z

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Observation 53fb11e2-9f9e-4445-8260-403254b0a9d2 · outbound

This paper cites Robust Neural IDA-PBC: Passivity-based stabilization under approximations,.

Safe Physics-Informed Machine Learning for Dynamics and Control Robust Neural IDA-PBC: Passivity-based stabilization under approximations,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.742829Z

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Observation 1650367d-1b25-480f-8cc8-77de33567a77 · outbound

This paper cites Stable Port-Hamiltonian Neural Networks,.

Safe Physics-Informed Machine Learning for Dynamics and Control Stable Port-Hamiltonian Neural Networks,

Reference 57

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Observation 6419ea3d-df28-4d63-abb3-db100667c17e · outbound

This paper cites Input convex neural networks,.

Safe Physics-Informed Machine Learning for Dynamics and Control Input convex neural networks,

Reference 58

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Observation fb34f441-350f-436f-97fb-3a560510c517 · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Safe Physics-Informed Machine Learning for Dynamics and Control Universal Differential Equations for Scientific Machine Learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.755381Z

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Observation 1554cb3d-37c8-479d-a0fb-0ed984ed4670 · outbound

This paper cites Structural inference of networked dynamical systems with universal differential equations,.

Safe Physics-Informed Machine Learning for Dynamics and Control Structural inference of networked dynamical systems with universal differential equations,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.759547Z

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Observation 996663ab-e169-4785-852f-c2db3a06e104 · outbound

This paper cites Neural differential algebraic equations,.

Safe Physics-Informed Machine Learning for Dynamics and Control Neural differential algebraic equations,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.763383Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Semi-explicit neural daes: Learning long-horizon dynamical systems with algebraic constraints,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.767403Z

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Safe Physics-Informed Machine Learning for Dynamics and Control A simultaneous approach for training neural differential-algebraic systems of equations,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.771250Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Neural port-hamiltonian differential algebraic equations for compositional learning of electrical networks,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.775528Z

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This paper cites Improving neural ordinary differential equations with nesterov's accelerated gradient method,.

Safe Physics-Informed Machine Learning for Dynamics and Control Improving neural ordinary differential equations with nesterov's accelerated gradient method,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.780013Z

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This paper cites Differentialequations.jl – a performant and feature-rich ecosystem for solving differential equations in julia,.

Safe Physics-Informed Machine Learning for Dynamics and Control Differentialequations.jl – a performant and feature-rich ecosystem for solving differential equations in julia,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.784344Z

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This paper cites Scenario Under- standing and Motion Prediction for Autonomous Vehicles - Review and Comparison,.

Safe Physics-Informed Machine Learning for Dynamics and Control Scenario Under- standing and Motion Prediction for Autonomous Vehicles - Review and Comparison,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.788234Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Social LSTM: Human Trajectory Prediction in Crowded Spaces,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.792018Z

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Safe Physics-Informed Machine Learning for Dynamics and Control THOMAS: Trajectory heatmap output with learned multi-agent sampling,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.795847Z

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Observation f3f33c90-9828-4aa4-aeb2-a3f94059d073 · outbound

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Safe Physics-Informed Machine Learning for Dynamics and Control Learning Lane Graph Representations for Motion Forecasting,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.800108Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Wayformer: Motion forecasting via simple and efficient attention networks,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.803936Z

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This paper cites Using online verification to prevent autonomous vehicles from causing accidents,.

Safe Physics-Informed Machine Learning for Dynamics and Control Using online verification to prevent autonomous vehicles from causing accidents,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.807525Z

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Safe Physics-Informed Machine Learning for Dynamics and Control SPOT: A tool for set-based prediction of traffic participants,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.811198Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Physics Constrained Motion Prediction with Uncertainty Quantification,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.815109Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Adversarial uncertainty quantification in physics-informed neural networks,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.818533Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems,

Reference 76

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This paper cites A review of uncertainty quantification in deep learning: Techniques, applications and challenges,.

Safe Physics-Informed Machine Learning for Dynamics and Control A review of uncertainty quantification in deep learning: Techniques, applications and challenges,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.825655Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Learning-based model predictive control: Toward safe learning in control,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.829383Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Autoip: A united framework to integrate physics into Gaussian processes,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.832879Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Physics-informed machine learning,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.836411Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Gaussian processes for regression,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.841204Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Unresolved cited work

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.845849Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Learning constrained dynamics with Gauss’ principle adhering Gaussian processes,

Reference 83

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Safe Physics-Informed Machine Learning for Dynamics and Control Physically consistent learning of conservative lagrangian systems with Gaussian processes,

Reference 84

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This paper cites Gaussian process port-Hamiltonian systems: Bayesian learning with physics prior,.

Safe Physics-Informed Machine Learning for Dynamics and Control Gaussian process port-Hamiltonian systems: Bayesian learning with physics prior,

Reference 85

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Safe Physics-Informed Machine Learning for Dynamics and Control Learning switching port-Hamiltonian systems with uncertainty quantification,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.862075Z

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This paper cites Physics-constrained learning for PDE systems with uncertainty quantified port-Hamiltonian models,.

Safe Physics-Informed Machine Learning for Dynamics and Control Physics-constrained learning for PDE systems with uncertainty quantified port-Hamiltonian models,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.866026Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Data-driven Bayesian control of port-Hamiltonian systems,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.870059Z

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This paper cites Conformal prediction: A gentle introduction,.

Safe Physics-Informed Machine Learning for Dynamics and Control Conformal prediction: A gentle introduction,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.873932Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Conformalized quantile regression,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.879232Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Rawlings, D

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.883071Z

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This paper cites Predictive control, embedded cyberphysical systems and systems of systems–a perspective,.

Safe Physics-Informed Machine Learning for Dynamics and Control Predictive control, embedded cyberphysical systems and systems of systems–a perspective,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.887606Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Diehl, H

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.891127Z

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This paper cites Linear offset-free model predictive control,.

Safe Physics-Informed Machine Learning for Dynamics and Control Linear offset-free model predictive control,

Reference 94

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Safe Physics-Informed Machine Learning for Dynamics and Control Model predictive control based on linear programming - the explicit solution,

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.898722Z

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This paper cites Robust model predictive control: A survey,.

Safe Physics-Informed Machine Learning for Dynamics and Control Robust model predictive control: A survey,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.902569Z

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This paper cites Stochastic model predictive control: An overview and perspectives for future research,.

Safe Physics-Informed Machine Learning for Dynamics and Control Stochastic model predictive control: An overview and perspectives for future research,

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.906521Z

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This paper cites Robust and stochastic model predictive control: Are we going in the right direction?.

Safe Physics-Informed Machine Learning for Dynamics and Control Robust and stochastic model predictive control: Are we going in the right direction?

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.910754Z

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This paper cites A com- putationally efficient robust model predictive control framework for uncertain nonlinear systems,.

Safe Physics-Informed Machine Learning for Dynamics and Control A com- putationally efficient robust model predictive control framework for uncertain nonlinear systems,

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-16T12:22:02.914640Z

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Safe Physics-Informed Machine Learning for Dynamics and Control Robust output feedback model predictive control of constrained linear systems: Time varying case,

Reference 100

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Observation 4b8bb0fb-db48-42cf-96ef-abe3e72ed9aa · inbound

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Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Safe Physics-Informed Machine Learning for Dynamics and Control

Reference 6

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Sparse Identification of Nonlinear Dynamics with Conformal Prediction Safe Physics-Informed Machine Learning for Dynamics and Control

Reference 5

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Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T18:15:37.210814Z

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Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:48.117810Z

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