Khemraj Shukla

Orcid: 0000-0002-2262-7264

According to our database1, Khemraj Shukla authored at least 22 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

2019
2020
2021
2022
2023
2024
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Online presence:

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Bibliography

2024
AI-Aristotle: A physics-informed framework for systems biology gray-box identification.
PLoS Comput. Biol., 2024

Tackling the curse of dimensionality with physics-informed neural networks.
Neural Networks, 2024

High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations.
J. Comput. Phys., 2024

Deep neural operators as accurate surrogates for shape optimization.
Eng. Appl. Artif. Intell., 2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements.
CoRR, 2024

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks.
CoRR, 2024

2023
High-order methods for hypersonic flows with strong shocks and real chemistry.
J. Comput. Phys., 2023

Rethinking materials simulations: Blending direct numerical simulations with neural operators.
CoRR, 2023

Randomized Forward Mode of Automatic Differentiation for Optimization Algorithms.
CoRR, 2023

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs.
CoRR, 2023

CrunchGPT: A chatGPT assisted framework for scientific machine learning.
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.
CoRR, 2023

Learning bias corrections for climate models using deep neural operators.
CoRR, 2023

Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils.
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.
IEEE Signal Process. Mag., 2022

Scalable algorithms for physics-informed neural and graph networks.
CoRR, 2022

Learning two-phase microstructure evolution using neural operators and autoencoder architectures.
CoRR, 2022

2021
Parallel physics-informed neural networks via domain decomposition.
J. Comput. Phys., 2021

A high order discontinuous Galerkin method for the symmetric form of the anisotropic viscoelastic wave equation.
Comput. Math. Appl., 2021

2020
A weight-adjusted discontinuous Galerkin method for the poroelastic wave equation: Penalty fluxes and micro-heterogeneities.
J. Comput. Phys., 2020

Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks.
CoRR, 2020

2019
Modeling the wave propagation in viscoacoustic media: An efficient spectral approach in time and space domain.
Comput. Geosci., 2019


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