Discover physical concepts and equations with machine learning | Paper Citation Record

Observation 94454eae-90bd-470a-9b3d-ca52a84e9650 · outbound

This paper cites Einstein, On the method of theoretical physics , Philos.

Discover physical concepts and equations with machine learning Einstein, On the method of theoretical physics , Philos

Reference 1

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Observation f0576781-0f68-40e2-9f34-18fa30f4eb4f · outbound

This paper cites Maillet, Heisenberg spin chains: from quantum groups to neutron scattering experiments, in Quantum Spaces, 161 (2007).

Discover physical concepts and equations with machine learning Maillet, Heisenberg spin chains: from quantum groups to neutron scattering experiments, in Quantum Spaces, 161 (2007)

Reference 2

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Observation 59c5e177-cf1b-4e86-a565-b8cc3f17f257 · outbound

This paper cites Wang et al.

Discover physical concepts and equations with machine learning Wang et al

Reference 3

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Observation 57d93950-bcc8-4222-9f38-59050993db09 · outbound

This paper cites Machine learning and the physical sciences.

Discover physical concepts and equations with machine learning Machine learning and the physical sciences

Reference 4

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Observation 0216446b-57fb-4020-a45e-17d3160b3153 · outbound

This paper cites Toward an AI Physicist for Unsupervised Learning.

Discover physical concepts and equations with machine learning Toward an AI Physicist for Unsupervised Learning

Reference 5

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Observation 88ab628e-d766-4c7f-951e-fe223039d158 · outbound

This paper cites AI Descartes: Combining Data and Theory for Derivable Scientific Discovery.

Discover physical concepts and equations with machine learning AI Descartes: Combining Data and Theory for Derivable Scientific Discovery

Reference 6

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Observation c2f3f4e4-061e-4219-a6f9-404b7505d63b · outbound

This paper cites On scientific understanding with artificial intelligence.

Discover physical concepts and equations with machine learning On scientific understanding with artificial intelligence

Reference 7

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Observation eeb00f9f-0b1f-4e4a-bf33-84ea2c176c89 · outbound

This paper cites Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert.

Discover physical concepts and equations with machine learning Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:27.384114Z

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Observation 6f17cca3-ddca-45de-912e-fdc89273dbd0 · outbound

This paper cites Discovering physical concepts with neural networks.

Discover physical concepts and equations with machine learning Discovering physical concepts with neural networks

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:27.360756Z

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Observation 8dc4dac9-ea17-4fd6-93c7-bb942f2254da · outbound

This paper cites Emergent Quantum Mechanics in an Introspective Machine Learning Architecture.

Discover physical concepts and equations with machine learning Emergent Quantum Mechanics in an Introspective Machine Learning Architecture

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:27.339068Z

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Observation 0eb9542d-c7bf-4761-b4d6-b666f07256a6 · outbound

This paper cites Machine learning the thermodynamic arrow of time.

Discover physical concepts and equations with machine learning Machine learning the thermodynamic arrow of time

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:27.316877Z

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Observation 6b3a41fe-b334-41de-961e-3741d2f39288 · outbound

This paper cites SymmetryGAN: Symmetry Discovery with Deep Learning.

Discover physical concepts and equations with machine learning SymmetryGAN: Symmetry Discovery with Deep Learning

Reference 12

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Observation 26910c7f-2e36-4937-8d9b-a323776d6880 · outbound

This paper cites A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning.

Discover physical concepts and equations with machine learning A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning

Reference 13

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Observation a6277202-5994-4e6e-89e3-722937cd279d · outbound

This paper cites Rediscovering orbital mechanics with machine learning.

Discover physical concepts and equations with machine learning Rediscovering orbital mechanics with machine learning

Reference 14

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Observation fd72c908-3345-45ca-aee2-f82181436e5c · outbound

This paper cites AI Poincar\'e: Machine Learning Conservation Laws from Trajectories.

Discover physical concepts and equations with machine learning AI Poincar\'e: Machine Learning Conservation Laws from Trajectories

Reference 15

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Observation 4993ebfd-00cc-4dfc-85b1-a890325d485c · outbound

This paper cites Noether Networks: Meta-Learning Useful Conserved Quantities.

Discover physical concepts and equations with machine learning Noether Networks: Meta-Learning Useful Conserved Quantities

Reference 16

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Observation ca20a4d0-0eea-4068-a28d-338922b9c733 · outbound

This paper cites AI Poincar\'{e} 2.0: Machine Learning Conservation Laws from Differential Equations.

Discover physical concepts and equations with machine learning AI Poincar\'{e} 2.0: Machine Learning Conservation Laws from Differential Equations

Reference 17

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Observation d53b53e0-c83d-4757-8e7d-24fb48609c3d · outbound

This paper cites Einstein, Science and Religion , Nature 146, 605 (1940).

Discover physical concepts and equations with machine learning Einstein, Science and Religion , Nature 146, 605 (1940)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.991365Z

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Observation bb074d87-eb61-4e04-90e3-394f57a9f4c7 · outbound

This paper cites Schr¨ odinger,An undulatory theory of the mechanics of atoms and molecules , Phys.

Discover physical concepts and equations with machine learning Schr¨ odinger,An undulatory theory of the mechanics of atoms and molecules , Phys

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.962051Z

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Observation 1851f0ab-3d69-42be-9b50-230ee269465d · outbound

This paper cites Neural Ordinary Differential Equations.

Discover physical concepts and equations with machine learning Neural Ordinary Differential Equations

Reference 20

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Observation 0d424943-6ea3-422f-aeaa-dc4df019aa34 · outbound

This paper cites Learning quantum dynamics with latent neural ODEs.

Discover physical concepts and equations with machine learning Learning quantum dynamics with latent neural ODEs

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:27.102775Z

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Observation f6049c85-9c83-47fe-b026-dedd3c4760a5 · outbound

This paper cites Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures.

Discover physical concepts and equations with machine learning Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.999259Z

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Observation 07fc6a91-822d-4adc-9af8-206a1d74c97b · outbound

This paper cites Sholokhov, Y.

Discover physical concepts and equations with machine learning Sholokhov, Y

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.886671Z

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Observation ba7fc007-b153-417a-a623-ff8031ca0c09 · outbound

This paper cites Automated adaptive inference of coarse-grained dynamical models in systems biology.

Discover physical concepts and equations with machine learning Automated adaptive inference of coarse-grained dynamical models in systems biology

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.883786Z

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Observation d357cbe1-7be7-48fb-b68d-1c656082f97e · outbound

This paper cites Auto-Encoding Variational Bayes.

Discover physical concepts and equations with machine learning Auto-Encoding Variational Bayes

Reference 25

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Observation 06d4a9c3-3e2d-4282-a2dc-7f1c9679553b · outbound

This paper cites Higgins et al., Beta-V AE: Learning basic visual concepts with a constrained varia- tional framework, ICLR, (2017).

Discover physical concepts and equations with machine learning Higgins et al., Beta-V AE: Learning basic visual concepts with a constrained varia- tional framework, ICLR, (2017)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.870278Z

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Observation e0f56e79-5de8-495a-8b7f-dff332a7f6b1 · outbound

This paper cites Explainable Representation Learning of Small Quantum States.

Discover physical concepts and equations with machine learning Explainable Representation Learning of Small Quantum States

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.845733Z

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Observation c5a3921c-f80d-4237-a4b2-5a0999877136 · outbound

This paper cites Fern´ andez-Fern´ andez et al., Learning minimal representations of stochas- tic processes with variational autoencoders , Phys.

Discover physical concepts and equations with machine learning Fern´ andez-Fern´ andez et al., Learning minimal representations of stochas- tic processes with variational autoencoders , Phys

Reference 28

Resolution: verified exact; raw_fallback, observed 2026-08-11T17:53:26.822193Z

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Observation 702b11c0-8cef-4789-be88-ed95f91fe919 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Discover physical concepts and equations with machine learning Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 29

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Observation 84aeb1a8-3f09-49b1-9a56-6b1cb895ab95 · outbound

This paper cites Discovering governing equations from data: Sparse identification of nonlinear dynamical systems.

Discover physical concepts and equations with machine learning Discovering governing equations from data: Sparse identification of nonlinear dynamical systems

Reference 30

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Observation f1509bd4-f896-4051-8936-cb4630d63157 · outbound

This paper cites Modern Koopman Theory for Dynamical Systems.

Discover physical concepts and equations with machine learning Modern Koopman Theory for Dynamical Systems

Reference 31

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Observation 462d11aa-34fb-4b97-a2f8-b4702963ac6d · outbound

This paper cites Neural ODE and Holographic QCD.

Discover physical concepts and equations with machine learning Neural ODE and Holographic QCD

Reference 32

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Observation 42fac6fb-dd1c-4acc-825b-05c18ea836ef · outbound

This paper cites Inference of neutrino flavor evolution through data assimilation and neural differential equations.

Discover physical concepts and equations with machine learning Inference of neutrino flavor evolution through data assimilation and neural differential equations

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.574293Z

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Observation 30268a87-57d6-4bc3-ae7e-cb2b5970d260 · outbound

This paper cites Chen et al., Forecasting the outcome of spintronic experiments with neural ordinary differential equations, Nat.

Discover physical concepts and equations with machine learning Chen et al., Forecasting the outcome of spintronic experiments with neural ordinary differential equations, Nat

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.854763Z

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Observation f9b3b762-81e0-49cb-9d01-e5ebafe9cbb2 · outbound

This paper cites Learning quantum dissipation by the neural ordinary differential equation.

Discover physical concepts and equations with machine learning Learning quantum dissipation by the neural ordinary differential equation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.552172Z

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Observation 8c4c025d-e90d-4c6c-9aa0-0a938b4b7522 · outbound

This paper cites Optical Neural Ordinary Differential Equations.

Discover physical concepts and equations with machine learning Optical Neural Ordinary Differential Equations

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.529852Z

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Observation e3863cd1-a73a-4d0e-abaf-55bb2db9ff0d · outbound

This paper cites Metalearning generalizable dynamics from trajectories.

Discover physical concepts and equations with machine learning Metalearning generalizable dynamics from trajectories

Reference 37

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Observation b95b88c7-480d-4bef-9633-986ae56f9e1f · outbound

This paper cites Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis.

Discover physical concepts and equations with machine learning Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis

Reference 38

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Observation b1d2ba2a-d4ce-41ee-98f5-98982383287a · outbound

This paper cites Neural ODEs for holographic transport models without translation symmetry.

Discover physical concepts and equations with machine learning Neural ODEs for holographic transport models without translation symmetry

Reference 39

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Observation 164e2b7d-dcda-4a4a-8dab-327b3674a1e3 · outbound

This paper cites Learning ODEs via Diffeomorphisms for Fast and Robust Integration.

Discover physical concepts and equations with machine learning Learning ODEs via Diffeomorphisms for Fast and Robust Integration

Reference 40

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Observation 682d60a6-0e72-4789-8b98-b77f306576b2 · outbound

This paper cites an unresolved cited work.

Discover physical concepts and equations with machine learning Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:53:27.838155Z

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Observation 81c63386-b3e6-49ad-a55c-2583a5fce391 · outbound

This paper cites Electron Spin or "Classically Non-Describable Two-Valuedness".

Discover physical concepts and equations with machine learning Electron Spin or "Classically Non-Describable Two-Valuedness"

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.275263Z

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Observation 3d0ffa1b-f7c6-49d6-b810-8a60a1e2ecff · outbound

This paper cites Pauli, Zur Quantenmechanik des magnetischen Elektrons , Z.

Discover physical concepts and equations with machine learning Pauli, Zur Quantenmechanik des magnetischen Elektrons , Z

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.821707Z

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This paper cites an unresolved cited work.

Discover physical concepts and equations with machine learning Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:53:27.804865Z

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Observation 226cdf09-fcc6-4815-ab82-2c12f345b89c · outbound

This paper cites Mining Behavioral Groups in Large Wireless LANs.

Discover physical concepts and equations with machine learning Mining Behavioral Groups in Large Wireless LANs

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.202118Z

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Observation d2a24482-59b1-48b7-b58d-02298b97b774 · outbound

This paper cites Neural Rough Differential Equations for Long Time Series.

Discover physical concepts and equations with machine learning Neural Rough Differential Equations for Long Time Series

Reference 46

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Observation af0473ef-3bd0-4c01-a8e4-093433083246 · outbound

This paper cites Latent Neural ODEs with Sparse Bayesian Multiple Shooting.

Discover physical concepts and equations with machine learning Latent Neural ODEs with Sparse Bayesian Multiple Shooting

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T17:53:25.308118Z

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Observation b69ec8f2-4b06-4549-8406-23985244b6a8 · outbound

This paper cites How to train your neural ODE: the world of Jacobian and kinetic regularization.

Discover physical concepts and equations with machine learning How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 48

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Observation 33c47273-9007-46b4-a5a5-11cfacaa37fa · outbound

This paper cites Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs.

Discover physical concepts and equations with machine learning Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:26.049399Z

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Observation 656cb88a-5c12-4e3b-9ef5-0381c561742e · outbound

This paper cites STEER: Simple Temporal Regularization For Neural ODEs.

Discover physical concepts and equations with machine learning STEER: Simple Temporal Regularization For Neural ODEs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T17:53:25.336809Z

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Observation a80ebad2-888f-47e8-bb9c-62c71e39cde8 · outbound

This paper cites Learning Differential Equations that are Easy to Solve.

Discover physical concepts and equations with machine learning Learning Differential Equations that are Easy to Solve

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:25.913870Z

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Observation d728ec96-54e0-467f-b507-7e4a1b275260 · outbound

This paper cites "Hey, that's not an ODE": Faster ODE Adjoints via Seminorms.

Discover physical concepts and equations with machine learning "Hey, that's not an ODE": Faster ODE Adjoints via Seminorms

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:25.888027Z

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Observation ce512648-b6b7-4770-b3ac-4f6e644ea0a9 · outbound

This paper cites Heavy Ball Neural Ordinary Differential Equations.

Discover physical concepts and equations with machine learning Heavy Ball Neural Ordinary Differential Equations

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:25.864901Z

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Observation 4b37328d-4afa-408b-b3b8-7f5b8448b367 · outbound

This paper cites an unresolved cited work.

Discover physical concepts and equations with machine learning Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:53:27.699945Z

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Observation d1d845e3-daa4-47ce-b5e5-dcb65b00b153 · outbound

This paper cites Zhao et al., Accelerating Neural ODEs: A variational formulation-based approach , ICLR, (2025).

Discover physical concepts and equations with machine learning Zhao et al., Accelerating Neural ODEs: A variational formulation-based approach , ICLR, (2025)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.627991Z

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Observation c78c3796-2e24-4cc2-991c-92d833a75714 · outbound

This paper cites Course and P.

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Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.541283Z

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Observation bff43429-8972-4349-acd5-73483b1cd848 · outbound

This paper cites Operationally meaningful representations of physical systems in neural networks.

Discover physical concepts and equations with machine learning Operationally meaningful representations of physical systems in neural networks

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:53:25.736475Z

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Observation ef237406-fa20-4bfb-b836-70eb51606fd1 · outbound

This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

Discover physical concepts and equations with machine learning LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T17:53:25.378239Z

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Unavailable: canonical work link unavailable.

Observation 669353a2-8a70-4faa-838d-9518955b08bb · outbound

This paper cites Wilczek, The Dirac Equation , International Journal of Modern Physics A , (2004).

Discover physical concepts and equations with machine learning Wilczek, The Dirac Equation , International Journal of Modern Physics A , (2004)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.524728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4f385e97-ea53-4d25-b477-e3de991b81c5 · outbound

This paper cites Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

Discover physical concepts and equations with machine learning Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T17:53:25.388264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a9adca0-b29a-44f8-a0f9-2ba5ea3c8683 · outbound

This paper cites Tsitouras, Runge–Kutta pairs of order 5(4) satisfying only the first column simpli- fying assumption , Comput.

Discover physical concepts and equations with machine learning Tsitouras, Runge–Kutta pairs of order 5(4) satisfying only the first column simpli- fying assumption , Comput

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.508495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7bd2508e-98ff-4a65-8f7b-23c57bcf6456 · outbound

This paper cites an unresolved cited work.

Discover physical concepts and equations with machine learning Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:53:27.490802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1a1db6f1-90e7-4af8-b41c-374329bda02b · outbound

This paper cites Basdevant, Lectures on Quantum Mechanics.

Discover physical concepts and equations with machine learning Basdevant, Lectures on Quantum Mechanics

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:53:27.473930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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