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09/19/2023
A learning-based multiscale model for reactive flow in porous media
We study solute-laden flow through permeable geological formations with ...
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06/21/2023
Learning Homogenization for Elliptic Operators
Multiscale partial differential equations (PDEs) arise in various applic...
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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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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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10/09/2020
Accelerated computational micromechanics
We present an approach to solving problems in micromechanics that is ame...
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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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05/07/2020
Model Reduction and Neural Networks for Parametric PDEs
We develop a general framework for data-driven approximation of input-ou...
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03/07/2020
Neural Operator: Graph Kernel Network for Partial Differential Equations
The classical development of neural networks has been primarily for mapp...
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12/13/2018