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06/07/2023
A novel deeponet model for learning moving-solution operators with applications to earthquake hypocenter localization
Seismicity induced by human activities poses a significant threat to pub...
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05/29/2023
Physics-Informed Computer Vision: A Review and Perspectives
Incorporation of physical information in machine learning frameworks are...
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04/26/2023
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
We introduce a new approach for solving forward systems of differential ...
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11/22/2022
High-Order Methods for Hypersonic Flows with Strong Shocks and Real Chemistry
We compare high-order methods including spectral difference (SD), flux r...
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08/16/2021
A physics-informed variational DeepONet for predicting the crack path in brittle materials
Failure trajectories, identifying the probable failure zones, and damage...
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12/23/2019
Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states
Predicting motions of vessels in extreme sea states represents one of th...
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10/29/2019
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central...
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08/02/2018
Machine Learning of Space-Fractional Differential Equations
Data-driven discovery of "hidden physics" -- i.e., machine learning of d...
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04/26/2016
