Khemraj Shukla

According to our database, Khemraj Shukla authored at least 19 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

High-order methods for hypersonic flows with strong shocks and real chemistry.

Ahmad Peyvan, Khemraj Shukla, Jesse Chan, George E. Karniadakis

J. Comput. Phys., 2023

Rethinking materials simulations: Blending direct numerical simulations with neural operators.

Vivek Oommen, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, George Em Karniadakis

CoRR, 2023

Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms.

Khemraj Shukla, Yeonjong Shin

CoRR, 2023

AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification.

Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla, George Em Karniadakis

CoRR, 2023

Tackling the Curse of Dimensionality with Physics-Informed Neural Networks.

Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi

CoRR, 2023

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

Jesse Chan, Khemraj Shukla, Xinhui Wu, Ruofeng Liu, Prani Nalluri

CoRR, 2023

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

Elham Kiyani, Mahdi Kooshkbaghi, Khemraj Shukla, Rahul Babu Koneru, Zhen Li, Luis Bravo, Anindya Ghoshal, George Em Karniadakis, Mikko Karttunen

CoRR, 2023

CrunchGPT: A chatGPT assisted framework for scientific machine learning.

Varun Kumar, Leonard Gleyzer, Adar Kahana, Khemraj Shukla, George Em Karniadakis

CoRR, 2023

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

Elham Kiyani, Khemraj Shukla, George Em Karniadakis, Mikko Karttunen

CoRR, 2023

Learning bias corrections for climate models using deep neural operators.

Aniruddha Bora, Khemraj Shukla, Shixuan Zhang, Bryce Harrop, Ruby Leung, George Em Karniadakis

CoRR, 2023

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

Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis

CoRR, 2023

2022

A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems.

Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, George Em Karniadakis

IEEE Signal Process. Mag., 2022

Scalable algorithms for physics-informed neural and graph networks.

Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George Em Karniadakis

CoRR, 2022

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

Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, George Em Karniadakis

CoRR, 2022

2021

Parallel physics-informed neural networks via domain decomposition.

Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis

J. Comput. Phys., 2021

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

Khemraj Shukla, Jesse Chan, Maarten V. de Hoop

Comput. Math. Appl., 2021

2020

A weight-adjusted discontinuous Galerkin method for the poroelastic wave equation: Penalty fluxes and micro-heterogeneities.

Khemraj Shukla, Jesse Chan, Maarten V. de Hoop, Priyank Jaiswal

J. Comput. Phys., 2020

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

Khemraj Shukla, Patricio Clark Di Leoni, James L. Blackshire, Daniel Sparkman, George Em Karniadakis

CoRR, 2020

2019

Modeling the wave propagation in viscoacoustic media: An efficient spectral approach in time and space domain.

Khemraj Shukla, José M. Carcione, Reynam da Cruz Pestana, Priyank Jaiswal, Turgut Özdenvar

Comput. Geosci., 2019