An Expert's Guide to Training Physics-informed Neural Networks | Paper Citation Record

Observation 5b581fd0-0289-42ea-ad58-e1ad5e508896 · inbound

Deep Learning Alternatives of the Kolmogorov Superposition Theorem cites this paper.

Deep Learning Alternatives of the Kolmogorov Superposition Theorem An Expert's Guide to Training Physics-informed Neural Networks

Reference 41

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ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks cites this paper.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks An Expert's Guide to Training Physics-informed Neural Networks

Reference 40

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Physics-informed neural networks for solving moving interface flow problems using the level set approach cites this paper.

Physics-informed neural networks for solving moving interface flow problems using the level set approach An Expert's Guide to Training Physics-informed Neural Networks

Reference 38

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Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures cites this paper.

Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures An Expert's Guide to Training Physics-informed Neural Networks

Reference 22

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Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement cites this paper.

Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement An Expert's Guide to Training Physics-informed Neural Networks

Reference 2004

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Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction cites this paper.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction An Expert's Guide to Training Physics-informed Neural Networks

Reference 33

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Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature cites this paper.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature An Expert's Guide to Training Physics-informed Neural Networks

Reference 49

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Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning cites this paper.

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning An Expert's Guide to Training Physics-informed Neural Networks

Reference 108

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BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs cites this paper.

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs An Expert's Guide to Training Physics-informed Neural Networks

Reference 37

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Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition cites this paper.

Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition An Expert's Guide to Training Physics-informed Neural Networks

Reference 33

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks cites this paper.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks An Expert's Guide to Training Physics-informed Neural Networks

Reference 32

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Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars cites this paper.

Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars An Expert's Guide to Training Physics-informed Neural Networks

Reference 20

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Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs cites this paper.

Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs An Expert's Guide to Training Physics-informed Neural Networks

Reference 46

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Estimation of Hemodynamic Parameters via Physics Informed Neural Networks including Hematocrit Dependent Rheology cites this paper.

Estimation of Hemodynamic Parameters via Physics Informed Neural Networks including Hematocrit Dependent Rheology An Expert's Guide to Training Physics-informed Neural Networks

Reference 43

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Continuously Tempered Diffusion Samplers cites this paper.

Continuously Tempered Diffusion Samplers An Expert's Guide to Training Physics-informed Neural Networks

Reference 26

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Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening cites this paper.

Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening An Expert's Guide to Training Physics-informed Neural Networks

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:36:53.671504Z

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LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries cites this paper.

LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries An Expert's Guide to Training Physics-informed Neural Networks

Reference 40

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PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation cites this paper.

PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation An Expert's Guide to Training Physics-informed Neural Networks

Reference 13

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Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method cites this paper.

Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method An Expert's Guide to Training Physics-informed Neural Networks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:36:35.265793Z

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Observation 182e0321-742f-4a88-8d8c-926a5fb6a27b · inbound

PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations cites this paper.

PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations An Expert's Guide to Training Physics-informed Neural Networks

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:51.730106Z

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Hard-constrained Physics-informed Neural Networks for Interface Problems cites this paper.

Hard-constrained Physics-informed Neural Networks for Interface Problems An Expert's Guide to Training Physics-informed Neural Networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:31:00.117672Z

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Observation 6edad4ed-fd38-4221-82cf-d51339b7cd87 · inbound

Hard-constrained Physics-informed Neural Networks for Interface Problems cites this paper.

Hard-constrained Physics-informed Neural Networks for Interface Problems An Expert's Guide to Training Physics-informed Neural Networks

Reference 4

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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 An Expert's Guide to Training Physics-informed Neural Networks

Reference 30

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Observation 6e7bef3e-ef62-4844-96b6-f06cc81e110b · inbound

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework cites this paper.

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework An Expert's Guide to Training Physics-informed Neural Networks

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:15:21.977838Z

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Observation 6b3ec7c7-17b9-4e90-8d0a-a3946d96bfa0 · inbound

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework cites this paper.

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework An Expert's Guide to Training Physics-informed Neural Networks

Reference 41

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Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle cites this paper.

Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle An Expert's Guide to Training Physics-informed Neural Networks

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:26:11.471387Z

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Deep-Learning based surrogate models for plasma exhaust simulations -- SOLPS-NN cites this paper.

Deep-Learning based surrogate models for plasma exhaust simulations -- SOLPS-NN An Expert's Guide to Training Physics-informed Neural Networks

Reference 29

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Transferable Physics-Informed Representations via Closed-Form Head Adaptation cites this paper.

Transferable Physics-Informed Representations via Closed-Form Head Adaptation An Expert's Guide to Training Physics-informed Neural Networks

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:49:15.740140Z

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Observation d2f39fc9-6c3f-4d93-8276-7dd175b37557 · inbound

A Deep Learning Approach to Describing the Plasma Sheath cites this paper.

A Deep Learning Approach to Describing the Plasma Sheath An Expert's Guide to Training Physics-informed Neural Networks

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:21:11.653755Z

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Observation 4b8088c0-1a36-4a31-bd9a-685ce76eb23f · inbound

A Deep Learning Approach to Describing the Plasma Sheath cites this paper.

A Deep Learning Approach to Describing the Plasma Sheath An Expert's Guide to Training Physics-informed Neural Networks

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:19:59.955654Z

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When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions cites this paper.

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions An Expert's Guide to Training Physics-informed Neural Networks

Reference 64

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Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations cites this paper.

Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations An Expert's Guide to Training Physics-informed Neural Networks

Reference 50

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AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training cites this paper.

AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training An Expert's Guide to Training Physics-informed Neural Networks

Reference 8

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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 An Expert's Guide to Training Physics-informed Neural Networks

Reference 44

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

Error whitening: Why Gauss-Newton outperforms Newton An Expert's Guide to Training Physics-informed Neural Networks

Reference 61

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Hermite-NGP: Gradient-Augmented Hash Encoding for Learning PDEs cites this paper.

Hermite-NGP: Gradient-Augmented Hash Encoding for Learning PDEs An Expert's Guide to Training Physics-informed Neural Networks

Reference 6

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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 An Expert's Guide to Training Physics-informed Neural Networks

Reference 68

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Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks cites this paper.

Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks An Expert's Guide to Training Physics-informed Neural Networks

Reference 6

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Observation 2fcaca3e-e56d-4be2-89aa-009ce3697cc4 · inbound

Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects cites this paper.

Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects An Expert's Guide to Training Physics-informed Neural Networks

Reference 4

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Observation bf7c4834-0ed4-499e-ae42-0c4f835b26f5 · inbound

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 An Expert's Guide to Training Physics-informed Neural Networks

Reference 132

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Observation 2e4b73da-20d7-4092-9998-cb2dc4f0afab · inbound

The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence cites this paper.

The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence An Expert's Guide to Training Physics-informed Neural Networks

Reference 18

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Observation fd10bff2-da1a-4b3e-ab8a-a14430acc46b · inbound

The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence cites this paper.

The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence An Expert's Guide to Training Physics-informed Neural Networks

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:04:37.838470Z

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Observation 0a4a871f-5bb5-41ff-ace0-75cc00c08f9b · inbound

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

Decision-Aware Evaluation of Physics-Informed Surrogates An Expert's Guide to Training Physics-informed Neural Networks

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:57:09.975367Z

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Observation 04967ed1-b3c5-4070-b7e3-7bcad3475e5b · inbound

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training cites this paper.

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training An Expert's Guide to Training Physics-informed Neural Networks

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:29.204463Z

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Observation 70c75ee5-51fd-41bf-bf05-2b95a115dffa · inbound

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks cites this paper.

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks An Expert's Guide to Training Physics-informed Neural Networks

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T22:29:00.680640Z

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Observation fc466fe5-ec6f-4f2c-b2de-7b45ffc7f407 · inbound

Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations cites this paper.

Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations An Expert's Guide to Training Physics-informed Neural Networks

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:29:29.693389Z

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Observation 5d221aec-15f6-47d2-829a-654a20320afa · inbound

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery cites this paper.

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery An Expert's Guide to Training Physics-informed Neural Networks

Reference 260

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.107703Z

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Observation 1f467796-443c-4a32-8dd1-482937293472 · inbound

A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks cites this paper.

A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks An Expert's Guide to Training Physics-informed Neural Networks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-25T19:28:16.784778Z

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Observation 7d1c8c27-fcbf-41d2-a649-80edcbfbd9e3 · inbound

Verified residual-specific explicit derivative kernels for physics-informed learning and discretized PDE adjoints cites this paper.

Verified residual-specific explicit derivative kernels for physics-informed learning and discretized PDE adjoints An Expert's Guide to Training Physics-informed Neural Networks

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:15:47.600379Z

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Observation 5c2e4aea-52ae-4227-9616-937df2efdeeb · inbound

Physics-informed Conditional Normalizing Flows for Angles-only Cislunar Orbit Determination cites this paper.

Physics-informed Conditional Normalizing Flows for Angles-only Cislunar Orbit Determination An Expert's Guide to Training Physics-informed Neural Networks

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-01T06:15:26.494964Z

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Observation 25ab3220-fdb2-4725-9e1e-8d5e04f41ec1 · inbound

Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors cites this paper.

Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors An Expert's Guide to Training Physics-informed Neural Networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-11T04:11:18.916745Z

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Observation 3cff4548-83a2-4982-ac4b-03d2d11817e3 · inbound

Trainable Spline Representations for Physics-Informed Learning cites this paper.

Trainable Spline Representations for Physics-Informed Learning An Expert's Guide to Training Physics-informed Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T22:28:10.030601Z

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Uncertainty quantification in mechanics: A unified Bayesian perspective cites this paper.

Uncertainty quantification in mechanics: A unified Bayesian perspective An Expert's Guide to Training Physics-informed Neural Networks

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Latent PDE mapping for efficient physics-informed learning across geometries with limited data cites this paper.

Latent PDE mapping for efficient physics-informed learning across geometries with limited data An Expert's Guide to Training Physics-informed Neural Networks

Reference 42

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Optimal Control with Expectation Constraint in a Smooth Boundary Case cites this paper.

Optimal Control with Expectation Constraint in a Smooth Boundary Case An Expert's Guide to Training Physics-informed Neural Networks

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Cardiovascular Digital Twins from Physics Based to Data Driven Approaches cites this paper.

Cardiovascular Digital Twins from Physics Based to Data Driven Approaches An Expert's Guide to Training Physics-informed Neural Networks

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