Efficient physics-informed neural networks using hash encoding

DOI10.1016/j.jcp.2024.112760arXiv2302.13397OpenAlexW4390765161WikidataQ130103759 ScholiaQ130103759MaRDI QIDQ6126546

Xinquan Huang, Tariq Alkhalifah

Publication date: 9 April 2024

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2302.13397

zbMATH Keywords

partial differential equationphysics-informed neural networkshash encoding

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Partial differential equations of mathematical physics and other areas of application (35Qxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Multilayer feedforward networks are universal approximators
• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
• Self-adaptive physics-informed neural networks
• Scientific machine learning through physics-informed neural networks: where we are and what's next
• Adaptive fourth-order phase field analysis for brittle fracture
• On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
• Exact fully 3D Navier–Stokes solutions for benchmarking
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

This page was built for publication: Efficient physics-informed neural networks using hash encoding