Paper Citation Record · LEDGER

Respecting causality is all you need for training physics-informed neural networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2203.07404.

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

pith.paper-citation-record.v1
2203.07404 v1

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measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 30 of 30 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:24.015596Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

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External citation measurements

18
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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Transformed Diffusion-Wave fPINNs: Enhancing Computing Efficiency for PINNs Solving Time-Fractional Diffusion-Wave Equations cites this paper.

Transformed Diffusion-Wave fPINNs: Enhancing Computing Efficiency for PINNs Solving Time-Fractional Diffusion-Wave Equations Respecting causality is all you need for training physics-informed neural networks

Reference 39

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Observation 8b8ee16a-40a4-40f5-84fd-fc3a5a04768d · inbound

Low-rank adaptive physics-informed HyperDeepONets for solving differential equations cites this paper.

Low-rank adaptive physics-informed HyperDeepONets for solving differential equations Respecting causality is all you need for training physics-informed neural networks

Reference 32

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Observation b9713eb4-98c0-4ebf-9835-146af4342b16 · inbound

Towards Digital Twins for Optimal Radioembolization cites this paper.

Towards Digital Twins for Optimal Radioembolization Respecting causality is all you need for training physics-informed neural networks

Reference 80

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Observation f4d6945c-66fd-4849-9435-b534874845e4 · inbound

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation cites this paper.

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation Respecting causality is all you need for training physics-informed neural networks

Reference 9

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Observation ca4a969c-62f6-4d6a-bd02-57f7b46b957c · inbound

Comparison of Trefftz-Based PINNs and Standard PINNs Focusing on Structure Preservation cites this paper.

Comparison of Trefftz-Based PINNs and Standard PINNs Focusing on Structure Preservation Respecting causality is all you need for training physics-informed neural networks

Reference 7

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Observation 5aafd4e8-5398-4051-b5be-c3fff6bdca9f · inbound

Exterior complex scaling enables physics-informed neural networks for quantum scattering cites this paper.

Exterior complex scaling enables physics-informed neural networks for quantum scattering Respecting causality is all you need for training physics-informed neural networks

Reference 44

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Observation 284425c1-a657-45fc-bc7f-1db0d2114891 · inbound

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs cites this paper.

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs Respecting causality is all you need for training physics-informed neural networks

Reference 54

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Observation a8f72252-2f3a-41d7-8099-2a670a9e24a5 · inbound

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs cites this paper.

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs Respecting causality is all you need for training physics-informed neural networks

Reference 54

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Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs cites this paper.

Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs Respecting causality is all you need for training physics-informed neural networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:20:26.521390Z

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Observation ee034030-5503-4e3b-b3af-770c8fce269d · inbound

PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs cites this paper.

PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs Respecting causality is all you need for training physics-informed neural networks

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:48:01.479895Z

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Observation c56b436c-aeee-4885-8140-0911da0c7ebf · inbound

The Design and Composition of Structural Causal Decision Processes cites this paper.

The Design and Composition of Structural Causal Decision Processes Respecting causality is all you need for training physics-informed neural networks

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:46:13.402344Z

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Observation 7b52f723-1612-4bc1-9703-6bc8eaa0c532 · inbound

Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks cites this paper.

Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks Respecting causality is all you need for training physics-informed neural networks

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:51:31.848745Z

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Observation 624798d8-3ea4-480d-80c3-0ef4bc157274 · inbound

Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks cites this paper.

Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks Respecting causality is all you need for training physics-informed neural networks

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:25:09.671908Z

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Observation 31b69a6b-08d5-417c-b3b5-13f03faa50af · inbound

Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems cites this paper.

Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems Respecting causality is all you need for training physics-informed neural networks

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:01:08.536113Z

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Observation 9d9fba7b-2457-4dd2-af9e-beb3be128d60 · inbound

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators cites this paper.

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators Respecting causality is all you need for training physics-informed neural networks

Reference 175

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:11:19.173858Z

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Observation 0532adab-e79c-49c4-a0af-70e74a1b93c3 · inbound

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks cites this paper.

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks Respecting causality is all you need for training physics-informed neural networks

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:46:26.585706Z

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Observation 6808d68d-27a2-48a2-8003-a081486f8c86 · inbound

Error whitening: Why Gauss-Newton outperforms Newton cites this paper.

Error whitening: Why Gauss-Newton outperforms Newton Respecting causality is all you need for training physics-informed neural networks

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:17:07.142582Z

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Observation 9df2935c-7ea9-461a-925c-84fc5b6033cc · inbound

MetaColloc: Optimization-Free PDE Solving via Meta-Learned Basis Functions cites this paper.

MetaColloc: Optimization-Free PDE Solving via Meta-Learned Basis Functions Respecting causality is all you need for training physics-informed neural networks

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:02:23.596179Z

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Random Neural Network Expressivity for Non-Linear Partial Differential Equations cites this paper.

Random Neural Network Expressivity for Non-Linear Partial Differential Equations Respecting causality is all you need for training physics-informed neural networks

Reference 67

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Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers cites this paper.

Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers Respecting causality is all you need for training physics-informed neural networks

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:34:02.767769Z

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Observation dfe7af61-dac7-4e1e-8286-c372d6bc5973 · inbound

Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies cites this paper.

Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies Respecting causality is all you need for training physics-informed neural networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-28T18:02:26.974942Z

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Observation 333f2393-d824-4baa-a488-aba9f607df31 · inbound

Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks cites this paper.

Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks Respecting causality is all you need for training physics-informed neural networks

Reference 20

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Uncovering Turbulent Dynamics in Stenotic Flows from 4D-flow MRI Measurements via Resolvent Analysis and Data Assimilation cites this paper.

Uncovering Turbulent Dynamics in Stenotic Flows from 4D-flow MRI Measurements via Resolvent Analysis and Data Assimilation Respecting causality is all you need for training physics-informed neural networks

Reference 131

Resolution: verified exact; arxiv_id, observed 2026-06-28T08:01:46.084429Z

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Observation f47c7e6a-0d12-46e6-b3a8-61d98d4d3756 · inbound

Decision-Aware Evaluation of Physics-Informed Surrogates cites this paper.

Decision-Aware Evaluation of Physics-Informed Surrogates Respecting causality is all you need for training physics-informed neural networks

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:57:09.972442Z

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Observation c951f908-ade3-4e99-b9ab-5c801f3ccd9b · inbound

Physics-Informed Neural Network with Squeeze-Excitation-like Attention cites this paper.

Physics-Informed Neural Network with Squeeze-Excitation-like Attention Respecting causality is all you need for training physics-informed neural networks

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:29:30.217064Z

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Observation ba800430-5b8e-465a-836b-7d3b1edae7ca · inbound

Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation cites this paper.

Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation Respecting causality is all you need for training physics-informed neural networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:09:58.737674Z

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Observation 5ec1e020-ba09-484d-9a67-f6c30d4b4618 · inbound

Generative wave propagator cites this paper.

Generative wave propagator Respecting causality is all you need for training physics-informed neural networks

Reference 33

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Observation c5555979-9dfc-4882-8449-ecb8643674a1 · inbound

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks cites this paper.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Respecting causality is all you need for training physics-informed neural networks

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-02T02:48:55.699909Z

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Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers cites this paper.

Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers Respecting causality is all you need for training physics-informed neural networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T06:09:02.605898Z

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Observation dd5030b3-7aa0-4afd-8e8a-82c3de8bedd4 · inbound

Variational Boosting for Physics-Informed Neural Networks cites this paper.

Variational Boosting for Physics-Informed Neural Networks Respecting causality is all you need for training physics-informed neural networks

Reference 2022

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