Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

DOI10.1098/rspa.2020.0334zbMath1472.68175arXiv1909.12228OpenAlexW3043174105WikidataQ98649573 ScholiaQ98649573MaRDI QIDQ5161023

Kenji Kawaguchi, Ameya D. Jagtap, George Em. Karniadakis

Publication date: 29 October 2021

Published in: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.12228

zbMATH Keywords

artificial intelligencemachine learningcomputational physicsapplied mathematicsphysics-informed neural networksstochastic gradientsbad minimadeep learning benchmarksaccelerated training

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Physics (00A79)

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Cites Work

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• Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
• Method of relaxed streamline upwinding for hyperbolic conservation laws
• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

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