Paper Citation Record · LEDGER

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models

As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.10930.

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pith.paper-citation-record.v1
2505.10930 v2

Coverage vector

measured 41 of 41 reference resolution

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Reference resolution

41 of 41 outbound references displayed

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Outbound references

Observation 1c72eccf-3e13-4e8a-9fff-e9093e27cede · outbound

This paper cites an unresolved cited work.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Unresolved cited work

Reference 1

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Observation 2b19e219-9256-4f71-a0c5-5d703cf5ae25 · outbound

This paper cites The total length of the testing dataset consists of 20 steps.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models The total length of the testing dataset consists of 20 steps

Reference 2

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Observation f00cb017-8b77-4f6b-8370-effd812905cc · outbound

This paper cites PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs

Reference 8

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Observation 25d5aab3-2c2a-43da-ac61-fd72d0cc5688 · outbound

This paper cites On the Foundations of Shortcut Learning.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models On the Foundations of Shortcut Learning

Reference 9

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Observation fb9766a4-8e3f-4b77-802e-51ee2b238b36 · outbound

This paper cites Geometry-Aware Gradient Algorithms for Neural Architecture Search.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Geometry-Aware Gradient Algorithms for Neural Architecture Search

Reference 11

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Observation f5f680ad-fa50-487f-8f53-5b3bfdfe236f · outbound

This paper cites Auxiliary Tasks in Multi-task Learning.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Auxiliary Tasks in Multi-task Learning

Reference 13

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Observation b2433f20-95b9-443b-89df-88a2a14a28fe · outbound

This paper cites Foundation Models for Geophysics: Review and Perspective.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Foundation Models for Geophysics: Review and Perspective

Reference 14

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Observation 06f10b83-d83b-4e6b-a51f-39568dc3784f · outbound

This paper cites PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics

Reference 15

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Observation 7136dc6e-5fd3-424e-8b63-3421f261d61e · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 16

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Observation 410a084e-cc7c-45de-b6c1-f12b52702d6a · outbound

This paper cites CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics

Reference 17

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Observation 14bdc1aa-92be-4074-9a4f-fbbcdaf077c9 · outbound

This paper cites Multiple Physics Pretraining for Physical Surrogate Models.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Multiple Physics Pretraining for Physical Surrogate Models

Reference 18

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Observation 64855994-d82b-4f19-bb11-1200a163350b · outbound

This paper cites On Causal and Anticausal Learning.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models On Causal and Anticausal Learning

Reference 19

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Observation 341673be-85f7-46b8-b24d-dd988dd43170 · outbound

This paper cites Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles

Reference 20

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Observation e3a67178-4578-42ce-96cc-ee81fd483ce9 · outbound

This paper cites Ups: Efficiently building foundation models for PDE solving via cross- modal adaptation.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Ups: Efficiently building foundation models for PDE solving via cross- modal adaptation

Reference 21

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Observation 5cec65c6-2a06-4fb7-b433-a731a5fe567b · outbound

This paper cites Shortcut Learning in In-Context Learning: A Survey.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Shortcut Learning in In-Context Learning: A Survey

Reference 22

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Observation ce1fd0c0-217e-4037-9be0-fbb0b760ebaf · outbound

This paper cites Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation

Reference 23

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Observation 54607c21-3c31-4b99-9313-db71fee65e16 · outbound

This paper cites Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning

Reference 24

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Observation 91ee18ef-06eb-4866-9f8e-a6e3d7e1d12c · outbound

This paper cites DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

Reference 27

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Observation d74492ac-d6dc-48bd-ab09-74c79c7ead3d · outbound

This paper cites PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 28

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Observation efbe8f45-eff9-429e-aa30-2e4097fe2e87 · outbound

This paper cites Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models

Reference 29

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This paper cites Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery

Reference 30

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Observation 08697b56-cb25-4fc1-993e-c3860022bbc9 · outbound

This paper cites PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 32

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Observation be8682ee-e9f2-4f7c-bcef-efe3862804d1 · outbound

This paper cites Masked Autoencoders are PDE Learners.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Masked Autoencoders are PDE Learners

Reference 33

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Observation d34ad860-a1f7-4500-90fa-68390cd36310 · outbound

This paper cites Strategies for Pretraining Neural Operators.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Strategies for Pretraining Neural Operators

Reference 34

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Observation b4ed660a-b1bb-4575-9a7f-0db238f375f4 · outbound

This paper cites The sequentially thresholded least squares (STLS) method (Budi ˇsi´c et al.,.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models The sequentially thresholded least squares (STLS) method (Budi ˇsi´c et al.,

Reference 38

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This paper cites STRidge time.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models STRidge time

Reference 39

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Reference 40

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Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Unresolved cited work

Reference 41

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Observation 6b1d26eb-0be7-40e9-94a4-1c414b97e62c · outbound

This paper cites Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

Reference 1973

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Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models and Efros, A

Reference 1997

Resolution: verified exact; raw_fallback, observed 2026-08-15T21:06:23.400650Z

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Observation b6a6121a-0081-41b3-962c-151ad3e70e80 · outbound

This paper cites Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models

Reference 2006

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Observation 5eee344d-792a-4775-a81a-50f48879c35a · outbound

This paper cites Autoregressive Action Sequence Learning for Robotic Manipulation.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Autoregressive Action Sequence Learning for Robotic Manipulation

Reference 2008

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This paper cites TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model

Reference 2012

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Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 2017

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Observation 711f8783-c170-4710-856d-4d51c04ce4bc · outbound

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

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Fourier Neural Operator for Parametric Partial Differential Equations

Reference 2019

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Observation b6aeb57e-822f-4496-bfcb-a7f06e89d8ad · outbound

This paper cites SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Reference 2020

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

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Towards Multi-spatiotemporal-scale Generalized PDE Modeling

Reference 2021

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Observation 1687997c-5a08-44d6-9ee9-75305ba31ed7 · outbound

This paper cites Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:06:23.716108Z

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Observation edd9db8d-912c-4326-9651-c8759008ad22 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Universal Language Model Fine-tuning for Text Classification

Reference 2023

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Observation 83f83ba2-5017-4047-b385-e62d9f32e0c0 · outbound

This paper cites OmniArch: Building Foundation Model For Scientific Computing.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models OmniArch: Building Foundation Model For Scientific Computing

Reference 2024

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Observation e520395a-d750-4480-bb80-cee43496ced6 · outbound

This paper cites Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation

Reference 2025

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:06:23.334309Z

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Pith citing papers

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