En-DeepONet: an enrichment approach for enhancing the expressivity of neural operators with applications to seismology

DOI10.1016/j.cma.2023.116681arXiv2306.04096OpenAlexW4390246622MaRDI QIDQ6194144

Umair bin Waheed, George Em. Karniadakis, Ehsan Haghighat

Publication date: 19 March 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2306.04096

zbMATH Keywords

machine learningneural operatorsearthquake localization

Mathematics Subject Classification ID

Computer science (68-XX) Quantum theory (81-XX)

Cites Work

• SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
• A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
• Improved architectures and training algorithms for deep operator networks
• A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
• A Viscosity Solutions Approach to Shape-From-Shading
• Fast Marching Methods
• A finite element method for crack growth without remeshing
• Error estimates for DeepONets: a deep learning framework in infinite dimensions
• Spectral/hp Element Methods for Computational Fluid Dynamics
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