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09/01/2023
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
We propose the geometry-informed neural operator (GINO), a highly effici...
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07/19/2021
Robust Topology Optimization Using Variational Autoencoders
Topology Optimization is the process of finding the optimal arrangement ...
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04/26/2021
Efficient training of physics-informed neural networks via importance sampling
Physics-Informed Neural Networks (PINNs) are a class of deep neural netw...
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12/14/2020
NVIDIA SimNet^TM: an AI-accelerated multi-physics simulation framework
We present SimNet, an AI-driven multi-physics simulation framework, to a...
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08/03/2020
Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlo
In this paper, we propose the Adaptive Physics-Informed Neural Networks ...
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10/11/2018
Physics-Informed Regularization of Deep Neural Networks
This paper presents a novel physics-informed regularization method for t...
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06/08/2018
A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
Developing efficient numerical algorithms for high dimensional random Pa...
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08/28/2017
