Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates | Paper Citation Record

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This paper cites Elsevier (2012).

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Elsevier (2012)

Reference 1

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Observation 783932c2-a0e3-470b-ba6a-6bb0e207a9d4 · outbound

This paper cites Bulletin of the American Mathematical Society (1967).

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Bulletin of the American Mathematical Society (1967)

Reference 2

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This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Fourier Neural Operator for Parametric Partial Differential Equations

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This paper cites Nature Machine Intelligence 3(3), 218–229 (2021) https://doi.org/10.1038/s42256-021-00302-5.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Nature Machine Intelligence 3(3), 218–229 (2021) https://doi.org/10.1038/s42256-021-00302-5

Reference 4

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This paper cites https://doi.org/10.5555/3648699.3648788.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates https://doi.org/10.5555/3648699.3648788

Reference 5

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This paper cites Choose a Transformer: Fourier or Galerkin.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Choose a Transformer: Fourier or Galerkin

Reference 6

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This paper cites Computers & Graphics 103, 201–211 (2022) https://doi.org/10.1016/j.cag.2022.02.004.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Computers & Graphics 103, 201–211 (2022) https://doi.org/10.1016/j.cag.2022.02.004

Reference 7

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This paper cites Interaction Networks for Learning about Objects, Relations and Physics.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Interaction Networks for Learning about Objects, Relations and Physics

Reference 8

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This paper cites Transformer for Partial Differential Equations' Operator Learning.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Transformer for Partial Differential Equations' Operator Learning

Reference 9

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This paper cites https://arxiv.org/abs/2402.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates https://arxiv.org/abs/2402

Reference 10

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This paper cites Computer Methods in Applied Mechanics and Engineering 426, 116983 (2024).

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Computer Methods in Applied Mechanics and Engineering 426, 116983 (2024)

Reference 11

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This paper cites AIAA Journal 58(1), 25–36 (2020) https://doi.org/10.2514/ 1.j058291.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates AIAA Journal 58(1), 25–36 (2020) https://doi.org/10.2514/ 1.j058291

Reference 12

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Observation c4f28a43-4b9a-4435-af48-cb29590a74a5 · outbound

This paper cites Towards Multi-spatiotemporal-scale Generalized PDE Modeling.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Towards Multi-spatiotemporal-scale Generalized PDE Modeling

Reference 13

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This paper cites Text2PDE: Latent Diffusion Models for Accessible Physics Simulation.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Text2PDE: Latent Diffusion Models for Accessible Physics Simulation

Reference 14

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This paper cites Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation

Reference 15

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This paper cites In: The Thirteenth International Conference on Learning Representations (2025).

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates In: The Thirteenth International Conference on Learning Representations (2025)

Reference 16

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This paper cites Scalable Transformer for PDE Surrogate Modeling.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Scalable Transformer for PDE Surrogate Modeling

Reference 17

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This paper cites Transolver: A Fast Transformer Solver for PDEs on General Geometries.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Transolver: A Fast Transformer Solver for PDEs on General Geometries

Reference 18

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This paper cites PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers

Reference 19

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This paper cites Message Passing Neural PDE Solvers.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Message Passing Neural PDE Solvers

Reference 20

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This paper cites Geometry-Informed Neural Operator for Large-Scale 3D PDEs.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Geometry-Informed Neural Operator for Large-Scale 3D PDEs

Reference 21

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This paper cites DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training

Reference 22

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Poseidon: Efficient Foundation Models for PDEs

Reference 23

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This paper cites Strategies for Pretraining Neural Operators.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Strategies for Pretraining Neural Operators

Reference 24

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This paper cites https://arxiv.org/abs/2403.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates https://arxiv.org/abs/2403

Reference 25

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This paper cites Nature Machine Intelligence 6(10), 1256–1269 (2024) https://doi.org/10.1038/s42256-024-00897-5.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Nature Machine Intelligence 6(10), 1256–1269 (2024) https://doi.org/10.1038/s42256-024-00897-5

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This paper cites Journal of Computational Physics 378, 686–707 (2019) https://doi.org/10.1016/j.jcp.2018.10.045.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Journal of Computational Physics 378, 686–707 (2019) https://doi.org/10.1016/j.jcp.2018.10.045

Reference 27

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates U-NO: U-shaped Neural Operators

Reference 28

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This paper cites Physics-Informed Neural Operator for Learning Partial Differential Equations.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Physics-Informed Neural Operator for Learning Partial Differential Equations

Reference 29

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates CaFA: Global Weather Forecasting with Factorized Attention on Sphere

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 31

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Learning Dissipative Dynamics in Chaotic Systems

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Lie Point Symmetry Data Augmentation for Neural PDE Solvers

Reference 33

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This paper cites Computer Methods in Applied Mechanics and Engineering 433, 117441 (2025) https://doi.org/10.1016/j.cma.2024.117441.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Computer Methods in Applied Mechanics and Engineering 433, 117441 (2025) https://doi.org/10.1016/j.cma.2024.117441

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates GraphCast: Learning skillful medium-range global weather forecasting

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates GenCast: Diffusion-based ensemble forecasting for medium-range weather

Reference 36

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Physics Informed Token Transformer for Solving Partial Differential Equations

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Learning Mesh-Based Simulation with Graph Networks

Reference 38

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Learning to Simulate Complex Physics with Graph Networks

Reference 39

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Learned Coarse Models for Efficient Turbulence Simulation

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Towards Physics-informed Deep Learning for Turbulent Flow Prediction

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Hamiltonian Graph Networks with ODE Integrators

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Unresolved cited work

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This paper cites Journal of Chem- ical Theory and Computation 16(8), 4757–4775 (2020) https://doi.org/10.1021/acs.jctc.0c00355 https://doi.org/10.1021/acs.jctc.0c00355.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Journal of Chem- ical Theory and Computation 16(8), 4757–4775 (2020) https://doi.org/10.1021/acs.jctc.0c00355 https://doi.org/10.1021/acs.jctc.0c00355

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This paper cites Chemical Reviews 121(16), 10142–10186 (2021) https:// doi.org/10.1021/acs.chemrev.0c01111 https://doi.org/10.1021/acs.chemrev.0c01111.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Chemical Reviews 121(16), 10142–10186 (2021) https:// doi.org/10.1021/acs.chemrev.0c01111 https://doi.org/10.1021/acs.chemrev.0c01111

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates The Journal of Chemical Physics 156(14), 144103 (2022) https://doi.org/10.1063/5.0083060 https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/5.0083060/16539506/144103 1 online.pdf

Reference 47

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This paper cites Proceedings of the National 20 Academy of Sciences 118(21), 2101784118 (2021) https://doi.org/10.1073/pnas.2101784118 https://www.pnas.org/doi/pdf/10.1073/pnas.2101784118.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Proceedings of the National 20 Academy of Sciences 118(21), 2101784118 (2021) https://doi.org/10.1073/pnas.2101784118 https://www.pnas.org/doi/pdf/10.1073/pnas.2101784118

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Unresolved cited work

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This paper cites Proceedings of the National Academy of Sciences 116(31), 15344–15349 (2019) https://doi.org/10.1073/pnas.1814058116 https://www.pnas.org/doi/pdf/10.1073/pnas.1814058116.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Proceedings of the National Academy of Sciences 116(31), 15344–15349 (2019) https://doi.org/10.1073/pnas.1814058116 https://www.pnas.org/doi/pdf/10.1073/pnas.1814058116

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This paper cites Computer Methods in Applied Mechanics and Engineering 420, 116692 (2024) https://doi.org/10.1016/j.cma.2023.116692.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Computer Methods in Applied Mechanics and Engineering 420, 116692 (2024) https://doi.org/10.1016/j.cma.2023.116692

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This paper cites Journal of the Mechanics and Physics of Solids 158, 104668 (2022) https://doi.org/10.1016/j.jmps.2021.104668.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Journal of the Mechanics and Physics of Solids 158, 104668 (2022) https://doi.org/10.1016/j.jmps.2021.104668

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This paper cites Mistani, P., Aragon-Calvo, M.A., Gibou, F.: Solving inverse-pde problems with physics- aware neural networks.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Mistani, P., Aragon-Calvo, M.A., Gibou, F.: Solving inverse-pde problems with physics- aware neural networks

Reference 53

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates galaxy model fitting

Reference 54

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Neural Ordinary Differential Equations

Reference 55

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 56

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Hamiltonian Neural Networks

Reference 57

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Lagrangian Neural Networks

Reference 58

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Reference 59

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Unresolved cited work

Reference 60

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This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Reference 61

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Attention Is All You Need

Reference 62

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Reference 63

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

Reference 64

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates https://arxiv.org/abs/2003

Reference 65

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Multipole Graph Neural Operator for Parametric Partial Differential Equations

Reference 66

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs

Reference 67

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 68

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Denoising Diffusion Probabilistic Models

Reference 69

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Wide Residual Networks

Reference 70

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This paper cites From Zero to Turbulence: Generative Modeling for 3D Flow Simulation.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates From Zero to Turbulence: Generative Modeling for 3D Flow Simulation

Reference 71

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs

Reference 72

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates https://arxiv.org/abs/2409

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:31:13.533631Z

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Observation dd01ba04-75fa-43c9-adc5-8dc20ff4cce7 · outbound

This paper cites Visualizing the Loss Landscape of Neural Nets.

Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Visualizing the Loss Landscape of Neural Nets

Reference 74

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Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates https://arxiv.org/abs/2207

Reference 75

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