Khemraj Shukla

40 papers Journal 40

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Drug Release Modeling using Physics-Informed Neural Networks. CoRR Daanish Aleem Qureshi, Khemraj Shukla, Vikas Srivastava
2026	J	`jnl`	Representation meets optimization: Training PINNs and PIKANs for gray-box discovery in systems pharmacology. Comput. Biol. Medicine Nazanin Ahmadi Daryakenari, Khemraj Shukla, George Em Karniadakis
2025	J	`jnl`	$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks. CoRR Júlia Vicens Figueres, Juliette Vanderhaeghen, Federica Bragone, Kateryna Morozovska, Khemraj Shukla
2025	J	`jnl`	A Neural Operator-Based Emulator for Regional Shallow Water Dynamics. CoRR Peter Rivera-Casillas, Sourav Dutta, Shukai Cai, Mark Loveland, Kamaljyoti Nath, Khemraj Shukla, Corey Jason Trahan, Jonghyun Lee, Matthew W. Farthing, Clint Dawson
2025	J	`jnl`	Deep Operator Neural Network Model Predictive Control. CoRR Thomas Oliver de Jong, Khemraj Shukla, Mircea Lazar
2025	J	`jnl`	Discovering Partially Known Ordinary Differential Equations: a Case Study on the Chemical Kinetics of Cellulose Degradation. CoRR Federica Bragone, Kateryna Morozovska, Tor Laneryd, Khemraj Shukla, Stefano Markidis
2025	J	`jnl`	FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design. CoRR Chenxi Wu, Juan Diego Toscano, Khemraj Shukla, Yingjie Chen, Ali Shahmohammadi, Edward Raymond, Thomas Toupy, Neda Nazemifard, Charles Papageorgiou, George Em Karniadakis
2025	J	`jnl`	GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics. CoRR Suguru Shiratori, Elham Kiyani, Khemraj Shukla, George Em Karniadakis
2025	J	`jnl`	Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI). CoRR Kamaljyoti Nath, Khemraj Shukla, Victor C. Tsai, Umair bin Waheed, Christian Huber, Omer Alpak, Chuen-Song Chen, Ligang Lu, Amik St.-Cyr
2025	J	`jnl`	Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology. CoRR Nazanin Ahmadi Daryakenari, Khemraj Shukla, George Em Karniadakis
2025	J	`jnl`	Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates. CoRR Aniruddha Bora, Shixuan Zhang, Khemraj Shukla, Bryce E. Harrop, George E. Karniadakis, L. Ruby Leung
2025	J	`jnl`	Which Optimizer Works Best for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks? CoRR Elham Kiyani, Khemraj Shukla, Jorge F. Urbán, Jérôme Darbon, George Em Karniadakis
2024	J	`jnl`	A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks. CoRR Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, Zongren Zou, George Em Karniadakis
2024	J	`jnl`	AI-Aristotle: A physics-informed framework for systems biology gray-box identification. PLoS Comput. Biol. Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla, George Em Karniadakis
2024	J	`jnl`	Deep neural operators as accurate surrogates for shape optimization. Eng. Appl. Artif. Intell. Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Nicholas Plewacki, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis
2024	J	`jnl`	High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations. J. Comput. Phys. Jesse Chan, Khemraj Shukla, Xinhui Wu, Ruofeng Liu, Prani Nalluri
2024	J	`jnl`	NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements. CoRR Khemraj Shukla, Zongren Zou, Chi Hin Chan, Additi Pandey, Zhicheng Wang, George Em Karniadakis
2024	J	`jnl`	Tackling the curse of dimensionality with physics-informed neural networks. Neural Networks Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi
2023	J	`jnl`	A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data. CoRR Elham Kiyani, Khemraj Shukla, George Em Karniadakis, Mikko Karttunen
2023	J	`jnl`	AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification. CoRR Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla, George Em Karniadakis
2023	J	`jnl`	Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs. CoRR Elham Kiyani, Mahdi Kooshkbaghi, Khemraj Shukla, Rahul Babu Koneru, Zhen Li, Luis Bravo, Anindya Ghoshal, George Em Karniadakis, Mikko Karttunen
2023	J	`jnl`	CrunchGPT: A chatGPT assisted framework for scientific machine learning. CoRR Varun Kumar, Leonard Gleyzer, Adar Kahana, Khemraj Shukla, George Em Karniadakis
2023	J	`jnl`	Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils. CoRR Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis
2023	J	`jnl`	High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations. CoRR Jesse Chan, Khemraj Shukla, Xinhui Wu, Ruofeng Liu, Prani Nalluri
2023	J	`jnl`	High-order methods for hypersonic flows with strong shocks and real chemistry. J. Comput. Phys. Ahmad Peyvan, Khemraj Shukla, Jesse Chan, George E. Karniadakis
2023	J	`jnl`	Learning bias corrections for climate models using deep neural operators. CoRR Aniruddha Bora, Khemraj Shukla, Shixuan Zhang, Bryce E. Harrop, L. Ruby Leung, George Em Karniadakis
2023	J	`jnl`	Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms. CoRR Khemraj Shukla, Yeonjong Shin
2023	J	`jnl`	Rethinking materials simulations: Blending direct numerical simulations with neural operators. CoRR Vivek Oommen, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, George Em Karniadakis
2023	J	`jnl`	Tackling the Curse of Dimensionality with Physics-Informed Neural Networks. CoRR Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi
2022	J	`jnl`	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. Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, George Em Karniadakis
2022	J	`jnl`	High-Order Methods for Hypersonic Flows with Strong Shocks and Real Chemistry. CoRR Ahmad Peyvan, Khemraj Shukla, Jesse Chan, George Em Karniadakis
2022	J	`jnl`	Learning two-phase microstructure evolution using neural operators and autoencoder architectures. CoRR Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, George Em Karniadakis
2022	J	`jnl`	Scalable algorithms for physics-informed neural and graph networks. CoRR Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George Em Karniadakis
2021	J	`jnl`	A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation. Comput. Math. Appl. Khemraj Shukla, Jesse Chan, Maarten V. de Hoop
2021	J	`jnl`	Parallel Physics-Informed Neural Networks via Domain Decomposition. CoRR Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
2021	J	`jnl`	Parallel physics-informed neural networks via domain decomposition. J. Comput. Phys. Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
2020	J	`jnl`	A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation. CoRR Khemraj Shukla, Jesse Chan, Maarten V. de Hoop
2020	J	`jnl`	A weight-adjusted discontinuous Galerkin method for the poroelastic wave equation: Penalty fluxes and micro-heterogeneities. J. Comput. Phys. Khemraj Shukla, Jesse Chan, Maarten V. de Hoop, Priyank Jaiswal
2020	J	`jnl`	Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks. CoRR Khemraj Shukla, Patricio Clark Di Leoni, James L. Blackshire, Daniel Sparkman, George Em Karniadakis
2019	J	`jnl`	Modeling the wave propagation in viscoacoustic media: An efficient spectral approach in time and space domain. Comput. Geosci. Khemraj Shukla, José M. Carcione, Reynam da Cruz Pestana, Priyank Jaiswal, Turgut Özdenvar