| Parallel physics-informed neural networks via domain decomposition K Shukla, AD Jagtap, GE Karniadakis Journal of Computational Physics 447, 110683, 2021 | 323 | 2021 |
| Physics‐informed neural networks (PINNs) for wave propagation and full waveform inversions M Rasht‐Behesht, C Huber, K Shukla, GE Karniadakis Journal of Geophysical Research: Solid Earth 127 (5), e2021JB023120, 2022 | 313 | 2022 |
| Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks K Shukla, PC Di Leoni, J Blackshire, D Sparkman, GE Karniadakis J Nondestruct Eval 39 (61), 2020 | 267 | 2020 |
| A physics-informed neural network for quantifying the microstructural properties of polycrystalline nickel using ultrasound data: A promising approach for solving inverse problems K Shukla, AD Jagtap, JL Blackshire, D Sparkman, GE Karniadakis IEEE Signal Processing Magazine 39 (1), 68-77, 2021 | 109 | 2021 |
| Learning two-phase microstructure evolution using neural operators and autoencoder architectures V Oommen, K Shukla, S Goswami, R Dingreville, GE Karniadakis nature (npj) computational materials 8 (190), 2022 | 107 | 2022 |
| Tackling the curse of dimensionality with physics-informed neural networks Z Hu, K Shukla, GE Karniadakis, K Kawaguchi Neural Networks 176, 106369, 2024 | 88 | 2024 |
| Earthquake hazard zonation of Sikkim Himalaya using a GIS platform I Pal, SK Nath, K Shukla, DK Pal, A Raj, KKS Thingbaijam, BK Bansal Natural hazards 45, 333-377, 2008 | 88 | 2008 |
| A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks K Shukla, JD Toscano, Z Wang, Z Zou, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 431, 117290, 2024 | 57 | 2024 |
| Deep neural operators as accurate surrogates for shape optimization K Shukla, V Oommen, A Peyvan, M Penwarden, N Plewacki, L Bravo, ... Engineering Applications of Artificial Intelligence 129, 107615, 2024 | 56* | 2024 |
| Scalable algorithms for physics-informed neural and graph networks K Shukla, M Xu, N Trask, GE Karniadakis Data-Centric Engineering 3, e24, 2022 | 45 | 2022 |
| Mycrunchgpt: A llm assisted framework for scientific machine learning V Kumar, L Gleyzer, A Kahana, K Shukla, GE Karniadakis Journal of Machine Learning for Modeling and Computing 4 (4), 2023 | 32 | 2023 |
| AI-Aristotle: A physics-informed framework for systems biology gray-box identification N Ahmadi Daryakenari, M De Florio, K Shukla, GE Karniadakis PLOS Computational Biology 20 (3), e1011916, 2024 | 30 | 2024 |
| A weight-adjusted discontinuous Galerkin method for the poroelastic wave equation: Penalty fluxes and micro-heterogeneities K Shukla, J Chan, MV de Hoop, P Jaiswal Journal of Computational Physics 403, 109061, 2020 | 28 | 2020 |
| Seismic hazard scenario and attenuation model of the Garhwal Himalaya using near-field synthesis from weak motion seismometry SK Nath, K Shukla, M Vyas Journal of earth system science 117, 649-670, 2008 | 28 | 2008 |
| A nodal discontinuous Galerkin finite element method for the poroelastic wave equation K Shukla, JS Hesthaven, JM Carcione, R Ye, J de la Puente, P Jaiswal Computational Geosciences 23, 595-615, 2019 | 21 | 2019 |
| High-order methods for hypersonic flows with strong shocks and real chemistry A Peyvan, K Shukla, J Chan, G Karniadakis Journal of Computational Physics 490, 112310, 2023 | 19 | 2023 |
| A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data E Kiyani, K Shukla, GE Karniadakis, M Karttunen Computer Methods in Applied Mechanics and Engineering, 415 (https://doi.org …, 2023 | 19 | 2023 |
| Machine learning as a seismic prior velocity model building method for full-waveform inversion: A case study from Colombia U Iturrarán-Viveros, AM Muñoz-García, O Castillo-Reyes, K Shukla Pure and Applied Geophysics 178 (2), 423-448, 2021 | 17 | 2021 |
| Waves at a fluid-solid interface: Explicit versus implicit formulation of boundary conditions using a discontinuous Galerkin method K Shukla, JM Carcione, JS Hesthaven, E L'heureux The Journal of the Acoustical Society of America 147 (5), 3136-3150, 2020 | 16 | 2020 |
| Rethinking materials simulations: Blending direct numerical simulations with neural operators V Oommen, K Shukla, S Desai, R Dingreville, GE Karniadakis npj Computational Materials 10 (1), 145, 2024 | 10 | 2024 |
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringZweryfikowany adres z brown.edu
