Khemraj Shukla

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 27

Top Topics

Numerical Simulations

Inverse Problems

Top Venues

J. Comput. Phys.

Comput. Math. Appl.

Eng. Appl. Artif. Intell.

Publications

Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, Zongren Zou, George Em Karniadakis
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks. CoRR (2024)
Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi
Tackling the curse of dimensionality with physics-informed neural networks. Neural Networks 176 (2024)
Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Nicholas Plewacki, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis
Deep neural operators as accurate surrogates for shape optimization. Eng. Appl. Artif. Intell. 129 (2024)
Jesse Chan, Khemraj Shukla, Xinhui Wu, Ruofeng Liu, Prani Nalluri
High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations. J. Comput. Phys. 504 (2024)
Khemraj Shukla, Zongren Zou, Chi Hin Chan, Additi Pandey, Zhicheng Wang, George Em Karniadakis
NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements. CoRR (2024)
Elham Kiyani, Mahdi Kooshkbaghi, Khemraj Shukla, Rahul Babu Koneru, Zhen Li, Luis Bravo, Anindya Ghoshal, George Em Karniadakis, Mikko Karttunen
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs. CoRR (2023)
Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla, George Em Karniadakis
AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification. CoRR (2023)
Jesse Chan, Khemraj Shukla, Xinhui Wu, Ruofeng Liu, Prani Nalluri
High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations. CoRR (2023)
Aniruddha Bora, Khemraj Shukla, Shixuan Zhang, Bryce Harrop, Ruby Leung, George Em Karniadakis
Learning bias corrections for climate models using deep neural operators. CoRR (2023)
Varun Kumar, Leonard Gleyzer, Adar Kahana, Khemraj Shukla, George Em Karniadakis
CrunchGPT: A chatGPT assisted framework for scientific machine learning. CoRR (2023)
Elham Kiyani, Khemraj Shukla, George Em Karniadakis, Mikko Karttunen
A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data. CoRR (2023)
Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils. CoRR (2023)
Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks. CoRR (2023)
Ahmad Peyvan, Khemraj Shukla, Jesse Chan, George E. Karniadakis
High-order methods for hypersonic flows with strong shocks and real chemistry. J. Comput. Phys. 490 (2023)
Vivek Oommen, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, George Em Karniadakis
Rethinking materials simulations: Blending direct numerical simulations with neural operators. CoRR (2023)
Khemraj Shukla, Yeonjong Shin
Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms. CoRR (2023)
Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, George Em Karniadakis
Learning two-phase microstructure evolution using neural operators and autoencoder architectures. CoRR (2022)
Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George Em Karniadakis
Scalable algorithms for physics-informed neural and graph networks. CoRR (2022)
Ahmad Peyvan, Khemraj Shukla, Jesse Chan, George Em Karniadakis
High-Order Methods for Hypersonic Flows with Strong Shocks and Real Chemistry. CoRR (2022)
Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, George Em Karniadakis
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems. IEEE Signal Process. Mag. 39 (1) (2022)
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
Parallel Physics-Informed Neural Networks via Domain Decomposition. CoRR (2021)
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
Parallel physics-informed neural networks via domain decomposition. J. Comput. Phys. 447 (2021)
Khemraj Shukla, Jesse Chan, Maarten V. de Hoop
A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation. Comput. Math. Appl. 99 (2021)
Khemraj Shukla, Patricio Clark Di Leoni, James L. Blackshire, Daniel Sparkman, George Em Karniadakis
Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks. CoRR (2020)
Khemraj Shukla, Jesse Chan, Maarten V. de Hoop
A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation. CoRR (2020)
Khemraj Shukla, Jesse Chan, Maarten V. de Hoop, Priyank Jaiswal
A weight-adjusted discontinuous Galerkin method for the poroelastic wave equation: Penalty fluxes and micro-heterogeneities. J. Comput. Phys. 403 (2020)
Khemraj Shukla, José M. Carcione, Reynam da Cruz Pestana, Priyank Jaiswal, Turgut Özdenvar
Modeling the wave propagation in viscoacoustic media: An efficient spectral approach in time and space domain. Comput. Geosci. 126 (2019)