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08/24/2023
Learning Only On Boundaries: a Physics-Informed Neural operator for Solving Parametric Partial Differential Equations in Complex Geometries
Recently deep learning surrogates and neural operators have shown promis...
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08/16/2023
An Expert's Guide to Training Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have been popularized as a deep...
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09/06/2022
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems
Deep Operator Networks (DeepONets) offer a powerful, data-driven tool fo...
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07/05/2022
Rethinking the Importance of Sampling in Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have emerged as a powerful tool...
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12/18/2020
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Physics-informed neural networks (PINNs) are demonstrating remarkable pr...
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05/09/2019
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
Machine learning techniques typically rely on large datasets to create a...
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11/28/2017
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
We introduce physics informed neural networks -- neural networks that ar...
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11/28/2017
