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05/31/2023
Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning
Recently, there has been significant interest in operator learning, i.e....
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02/02/2023
Convolutional Neural Operators
Although very successfully used in machine learning, convolution based n...
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01/26/2023
Neural Inverse Operators for Solving PDE Inverse Problems
A large class of inverse problems for PDEs are only well-defined as mapp...
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10/03/2022
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities
A large class of hyperbolic and advection-dominated PDEs can have soluti...
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07/18/2022
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
Physics informed neural networks (PINNs) require regularity of solutions...
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04/12/2021
Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs
We propose a novel algorithm, based on physics-informed neural networks ...
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09/25/2020
Physics Informed Neural Networks for Simulating Radiative Transfer
We propose a novel machine learning algorithm for simulating radiative t...
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06/29/2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs II: A class of inverse problems
Physics informed neural networks (PINNs) have recently been very success...
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06/29/2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs
Physics informed neural networks (PINNs) have recently been widely used ...
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09/20/2019