George Karniadakis | DeepAI

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Featured Co-authors

Clinton Fookes
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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...

0 Ehsan Haghighat, et al. ∙

∙ 05/29/2023

Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are...

0 Chayan Banerjee, et al. ∙

∙ 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 ...

0 Simin Shekarpaz, et al. ∙

∙ 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...

0 Ahmad Peyvan, et al. ∙

∙ 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...

4 Somdatta Goswami, et al. ∙

∙ 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...

0 José del Águila Ferrandis, et al. ∙

∙ 10/29/2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central...

42 Liu Yang, et al. ∙

∙ 08/02/2018

Machine Learning of Space-Fractional Differential Equations

Data-driven discovery of "hidden physics" -- i.e., machine learning of d...

0 Mamikon Gulian, et al. ∙

∙ 04/26/2016

Deep Multi-fidelity Gaussian Processes

We develop a novel multi-fidelity framework that goes far beyond the cla...

0 Maziar Raissi, et al. ∙

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