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12/28/2022
A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems
This paper concerns the study of optimal (supremum and infimum) uncertai...
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07/11/2022
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
Deep learning surrogate models have shown promise in solving partial dif...
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05/27/2022
Learning Markovian Homogenized Models in Viscoelasticity
Fully resolving dynamics of materials with rapidly-varying features invo...
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11/06/2021
Physics-Informed Neural Operator for Learning Partial Differential Equations
Machine learning methods have recently shown promise in solving partial ...
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08/19/2021
Neural Operator: Learning Maps Between Function Spaces
The classical development of neural networks has primarily focused on le...
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06/13/2021
Markov Neural Operators for Learning Chaotic Systems
Chaotic systems are notoriously challenging to predict because of their ...
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10/18/2020
Fourier Neural Operator for Parametric Partial Differential Equations
The classical development of neural networks has primarily focused on le...
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06/16/2020
Multipole Graph Neural Operator for Parametric Partial Differential Equations
One of the main challenges in using deep learning-based methods for simu...
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03/07/2020
