:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Roy, Pratanu, Castonguay, Stephen
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computational Physics
Online Access:	https://arxiv.org/abs/2403.03223
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Hard-constrained Physics-informed Neural Networks for Interface Problems
by: Chung, Seung Whan, et al.
Published: (2026)

$ϕ-$DeepONet: A Discontinuity Capturing Neural Operator
by: Roy, Sumanta, et al.
Published: (2026)

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
by: Roy, Sumanta, et al.
Published: (2024)

ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
by: Chen, Wenqian, et al.
Published: (2026)

Exactness Matters for Physical Rule Enforcement
by: Kim, Bum Jun
Published: (2026)

Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
by: Chiu, Pao-Hsiung, et al.
Published: (2026)

Statistical Learning Analysis of Physics-Informed Neural Networks
by: Barajas-Solano, David A.
Published: (2026)

Evidential Physics-Informed Neural Networks for Scientific Discovery
by: Tan, Hai Siong, et al.
Published: (2025)

Evidential Physics-Informed Neural Networks
by: Tan, Hai Siong, et al.
Published: (2025)

Quantum Noise Tomography with Physics-Informed Neural Networks
by: Sulc, Antonin
Published: (2025)

On the Role of Consistency Between Physics and Data in Physics-Informed Neural Networks
by: Becerra-Zuniga, Nicolás, et al.
Published: (2026)

Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
by: Dangel, Felix, et al.
Published: (2024)

Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
by: Hoshisashi, Kentaro, et al.
Published: (2026)

PINGS: Physics-Informed Neural Network for Fast Generative Sampling
by: Prasha, Achmad Ardani, et al.
Published: (2025)

DiffGrad for Physics-Informed Neural Networks
by: Rahman, Jamshaid Ul, et al.
Published: (2024)

Bayesian Reasoning for Physics Informed Neural Networks
by: Graczyk, Krzysztof M., et al.
Published: (2023)

Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding
by: Arni, Rohan, et al.
Published: (2025)

Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks
by: Haywood-Alexander, Marcus, et al.
Published: (2024)

Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
by: Naujoks, Jonas R., et al.
Published: (2025)

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
by: Jekic, Aleksandra, et al.
Published: (2025)

PINNfluence: Influence Functions for Physics-Informed Neural Networks
by: Naujoks, Jonas R., et al.
Published: (2024)

Generalized Lie Symmetries in Physics-Informed Neural Operators
by: Wang, Amy Xiang, et al.
Published: (2025)

Physics-Informed Neural Solvers for Periodic Quantum Eigenproblems
by: Mian, Haaris
Published: (2025)

Taper-based scattering formulation of the Helmholtz equation to improve the training process of Physics-Informed Neural Networks
by: Dörfler, W., et al.
Published: (2024)

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks
by: Sobh, Nahil, et al.
Published: (2025)

Energy Dissipation Preserving Physics Informed Neural Network for Allen-Cahn Equations
by: Kütük, Mustafa, et al.
Published: (2024)

Physics-Informed Neural Network based Damage Identification for Truss Railroad Bridges
by: Shajihan, Althaf, et al.
Published: (2025)

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
by: Rao, Xiang, et al.
Published: (2025)

Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems
by: Zhou, Yuqing, et al.
Published: (2026)

EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
by: Eivazi, Hamidreza, et al.
Published: (2025)

Inverse Modeling of Dielectric Response in Time Domain using Physics-Informed Neural Networks
by: Esenov, Emir, et al.
Published: (2025)

Training Deep Physics-Informed Kolmogorov-Arnold Networks
by: Rigas, Spyros, et al.
Published: (2025)

Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
by: Moseley, Ben, et al.
Published: (2021)

Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows
by: Cheng, Xinlun, et al.
Published: (2025)

Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy
by: Lee, Wei Shan, et al.
Published: (2025)

Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
by: Urbán, Jorge F., et al.
Published: (2024)

Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
by: Iyer, Narayan S, et al.
Published: (2025)

Supervised Metric Regularization Through Alternating Optimization for Multi-Regime Physics-Informed Neural Networks
by: Spotorno, Enzo Nicolas, et al.
Published: (2026)

Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy
by: Li, Zhihui, et al.
Published: (2023)

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
by: Flores, Pablo, et al.
Published: (2025)