:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Hoshisashi, Kentaro, Phelan, Carolyn E, Barucca, Paolo
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computational Physics
Online Access:	https://arxiv.org/abs/2604.13723
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface
by: Hoshisashi, Kentaro, et al.
Published: (2024)

No-Arbitrage Deep Calibration for Volatility Smile and Skewness
by: Hoshisashi, Kentaro, et al.
Published: (2023)

QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
by: Farea, Afrah, et al.
Published: (2025)

Walsh-Hadamard Neural Operators for Solving PDEs with Discontinuous Coefficients
by: Cavallazzi, Giorgio M., et al.
Published: (2025)

Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
by: Kong, Chun-Wei, et al.
Published: (2024)

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

LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
by: Wang, Jing, et al.
Published: (2025)

Parameterized Physics-informed Neural Networks for Parameterized PDEs
by: Cho, Woojin, et al.
Published: (2024)

Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs
by: Škardová, Kateřina, et al.
Published: (2024)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
by: Lei, Guanhang, et al.
Published: (2023)

Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems
by: Thiruthummal, Abhiram Anand, 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)

Loss Jump During Loss Switch in Solving PDEs with Neural Networks
by: Wang, Zhiwei, et al.
Published: (2024)

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
by: Lee, Wei Shan, et al.
Published: (2025)

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)

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
by: Zhai, Jucai, et al.
Published: (2026)

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)

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

Physics Informed Token Transformer for Solving Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2023)

TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
by: Chen, Zhuo, et al.
Published: (2024)

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)

Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks
by: Roy, Pratanu, et al.
Published: (2024)

Self-Adaptive Physics-Informed Quantum Machine Learning for Solving Differential Equations
by: Setty, Abhishek, et al.
Published: (2023)

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2025)

General Fourier Feature Physics-Informed Extreme Learning Machine (GFF-PIELM) for High-Frequency PDEs
by: Ren, Fei, 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)

DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
by: Jiang, Zhaoxi, et al.
Published: (2025)

Empirical Stability Analysis of Kolmogorov-Arnold Networks in Hard-Constrained Recurrent Physics-Informed Discovery
by: Spotorno, Enzo Nicolas, et al.
Published: (2026)

Granger Causality Detection with Kolmogorov-Arnold Networks
by: Lin, Hongyu, 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)

DRIFT-Net: A Spectral--Coupled Neural Operator for PDEs Learning
by: Li, Jiayi, 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)