Khemraj Shukla

According to our database, Khemraj Shukla authored at least 36 papers between 2019 and 2026.

Collaborative distances:

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

2026

Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles.

Khemraj Shukla, Zongren Zou, Theo Käufer, Michael Triantafyllou, George Em Karniadakis

CoRR, April, 2026

Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks.

Anas Jnini, Elham Kiyani, Khemraj Shukla, Jorge F. Urbán, Nazanin Ahmadi Daryakenari, Johannes Muller, Marius Zeinhofer, George Em Karniadakis

CoRR, April, 2026

Drug Release Modeling using Physics-Informed Neural Networks.

Daanish Aleem Qureshi, Khemraj Shukla, Vikas Srivastava

CoRR, February, 2026

Complex valued Deep Operator Network (DeepONet) [G] for three dimensional Maxwell's equations: G∈Cm×n.

Qile Jiang, Marc Salvadori, Dale Ota, Vijaya Shankar, Khemraj Shukla

J. Comput. Phys., 2026

Representation meets optimization: Training PINNs and PIKANs for gray-box discovery in systems pharmacology.

Nazanin Ahmadi Daryakenari, Khemraj Shukla, George Em Karniadakis

Comput. Biol. Medicine, 2026

2025

GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics.

Suguru Shiratori, Elham Kiyani, Khemraj Shukla, George Em Karniadakis

CoRR, December, 2025

Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates.

Aniruddha Bora, Shixuan Zhang, Khemraj Shukla, Bryce E. Harrop, George E. Karniadakis, L. Ruby Leung

CoRR, December, 2025

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design.

Chenxi Wu, Juan Diego Toscano, Khemraj Shukla, Yingjie Chen, Ali Shahmohammadi, Edward Raymond, Thomas Toupy, Neda Nazemifard, Charles Papageorgiou, George Em Karniadakis

CoRR, May, 2025

Deep Operator Neural Network Model Predictive Control.

Thomas Oliver de Jong, Khemraj Shukla, Mircea Lazar

CoRR, May, 2025

$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks.

Júlia Vicens Figueres, Juliette Vanderhaeghen, Federica Bragone, Kateryna Morozovska, Khemraj Shukla

CoRR, April, 2025

Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI).

Kamaljyoti Nath, Khemraj Shukla, Victor C. Tsai, Umair bin Waheed, Christian Huber, Omer Alpak, Chuen-Song Chen, Ligang Lu, Amik St.-Cyr

CoRR, April, 2025

Discovering Partially Known Ordinary Differential Equations: a Case Study on the Chemical Kinetics of Cellulose Degradation.

Federica Bragone, Kateryna Morozovska, Tor Laneryd, Khemraj Shukla, Stefano Markidis

CoRR, April, 2025

A Neural Operator-Based Emulator for Regional Shallow Water Dynamics.

Peter Rivera-Casillas, Sourav Dutta, Shukai Cai, Mark Loveland, Kamaljyoti Nath, Khemraj Shukla, Corey Jason Trahan, Jonghyun Lee, Matthew W. Farthing, Clint Dawson

CoRR, February, 2025

Which Optimizer Works Best for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks?

Elham Kiyani, Khemraj Shukla, Jorge F. Urbán, Jérôme Darbon, George Em Karniadakis

CoRR, January, 2025

2024

AI-Aristotle: A physics-informed framework for systems biology gray-box identification.

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

PLoS Comput. Biol., 2024

Tackling the curse of dimensionality with physics-informed neural networks.

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

Neural Networks, 2024

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

J. Comput. Phys., 2024

Deep neural operators as accurate surrogates for shape optimization.

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

Eng. Appl. Artif. Intell., 2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements.

Khemraj Shukla, Zongren Zou, Chi Hin Chan, Additi Pandey, Zhicheng Wang, George Em Karniadakis

CoRR, 2024

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks.

Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, Zongren Zou, George Em Karniadakis

CoRR, 2024

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

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 E. Harrop, L. 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