Khemraj Shukla | DeepAI

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∙ 07/23/2023

Tackling the Curse of Dimensionality with Physics-Informed Neural Networks

The curse-of-dimensionality (CoD) taxes computational resources heavily ...

0 Zheyuan Hu, et al. ∙

∙ 07/22/2023

High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations

High order schemes are known to be unstable in the presence of shock dis...

0 Jesse Chan, et al. ∙

∙ 07/18/2023

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs

The molten sand, a mixture of calcia, magnesia, alumina, and silicate, k...

0 Elham Kiyani, et al. ∙

∙ 06/27/2023

CrunchGPT: A chatGPT assisted framework for scientific machine learning

Scientific Machine Learning (SciML) has advanced recently across many di...

0 Varun Kumar, et al. ∙

∙ 05/18/2023

A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data

We propose a framework and an algorithm to uncover the unknown parts of ...

0 Elham Kiyani, et al. ∙

∙ 02/07/2023

Learning bias corrections for climate models using deep neural operators

Numerical simulation for climate modeling resolving all important scales...

0 Aniruddha Bora, et al. ∙

∙ 02/02/2023

Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils

Deep neural operators, such as DeepONets, have changed the paradigm in h...

0 Khemraj Shukla, 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. ∙

∙ 05/16/2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new ...

20 Khemraj Shukla, et al. ∙

∙ 04/11/2022

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Phase-field modeling is an effective mesoscale method for capturing the ...

8 Vivek Oommen, et al. ∙

∙ 04/20/2021

Parallel Physics-Informed Neural Networks via Domain Decomposition

We develop a distributed framework for the physics-informed neural netwo...

1 Khemraj Shukla, et al. ∙

∙ 10/31/2020

A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation

Wave propagation in real media is affected by various non-trivial physic...

0 Khemraj Shukla, et al. ∙

∙ 05/07/2020

Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks

We introduce an optimized physics-informed neural network (PINN) trained...

15 Khemraj Shukla, et al. ∙

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