440 citations · 851 across the 27 of their papers we have counts for
28 papers
GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics
Suguru Shiratori, Elham Kiyani, Khemraj Shukla +1
We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are…
Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
Aniruddha Bora, Shixuan Zhang, Khemraj Shukla +3
Coarse resolution, imperfect parameterizations, and uncertain initial states and forcings limit Earth-system model (ESM) predictions. Traditional bias correction via data assimilat…
H3PC: Hypersonic, High-Order, High-Performance Code with Adaptive Mesh Refinement and Real Chemistry
Ahmad Peyvan, Khemraj Shukla, George Em Karniadakis
We have developed a hypersonic high-order, high-performance code (HPC) utilizing the ``Trixi.jl" framework in order to simulate both non-reactive and chemically reactive compre…
Body-Free Simulation of Three-Dimensional Turbulent Cylinder Wakes
Zhicheng Wang, Theo Käufer, Khemraj Shukla +2
We present a body-free simulation framework for three-dimensional turbulent cylinder wakes, in which the upstream cylinder is not explicitly resolved. Instead, the incompressible N…
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design
Chenxi Wu, Juan Diego Toscano, Khemraj Shukla +7
We propose FMEnets, a physics-informed machine learning framework for the design and analysis of non-ideal plug flow reactors. FMEnets integrates the fundamental governing equation…
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology
Nazanin Ahmadi Daryakenari, Khemraj Shukla, George Em Karniadakis
Physics-Informed Kolmogorov-Arnold Networks (PIKANs) are gaining attention as an effective counterpart to the original multilayer perceptron-based Physics-Informed Neural Networks…
Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
Elham Kiyani, Khemraj Shukla, Jorge F. Urbán +2
Physics-Informed Neural Networks (PINNs) have revolutionized the computation of PDE solutions by integrating partial differential equations (PDEs) into the neural network's trainin…
NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements
Khemraj Shukla, Zongren Zou, Chi Hin Chan +3
Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due…
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang +2
Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning mode…
Deep operator learning-based surrogate models for aerothermodynamic analysis of AEDC hypersonic waverider
Khemraj Shukla, Jasmine Ratchford, Luis Bravo +4
Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that dee…
Rethinking materials simulations: Blending direct numerical simulations with neural operators
Vivek Oommen, Khemraj Shukla, Saaketh Desai +2
Direct numerical simulations (DNS) are accurate but computationally expensive for predicting materials evolution across timescales, due to the complexity of the underlying evolutio…
AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification
Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla +1
Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-inform…
MyCrunchGPT: A chatGPT assisted framework for scientific machine learning
Varun Kumar, Leonard Gleyzer, Adar Kahana +2
Scientific Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seaml…
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 +6
The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of…
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
Zheyuan Hu, Khemraj Shukla, George Em Karniadakis +1
The curse-of-dimensionality taxes computational resources heavily with exponentially increasing computational cost as the dimension increases. This poses great challenges in solvin…
High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations
Jesse Chan, Khemraj Shukla, Xinhui Wu +2
High order schemes are known to be unstable in the presence of shock discontinuities or under-resolved solution features for nonlinear conservation laws. Entropy stable schemes add…
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 +1
We propose a framework and an algorithm to uncover the unknown parts of nonlinear equations directly from data. The framework is based on eXtended Physics-Informed Neural Networks…
Learning bias corrections for climate models using deep neural operators
Aniruddha Bora, Khemraj Shukla, Shixuan Zhang +3
Numerical simulation for climate modeling resolving all important scales is a computationally taxing process. Therefore, to circumvent this issue a low resolution simulation is per…
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Khemraj Shukla, Vivek Oommen, Ahmad Peyvan +5
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving…
High-Order Methods for Hypersonic Flows with Strong Shocks and Real Chemistry
Ahmad Peyvan, Khemraj Shukla, Jesse Chan +1
We compare high-order methods including spectral difference (SD), flux reconstruction (FR), the entropy-stable discontinuous Galerkin spectral element method (ES-DGSEM), modal disc…
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla, Mengjia Xu, Nathaniel Trask +1
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale proc…
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Vivek Oommen, Khemraj Shukla, Somdatta Goswami +2
Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and gener…
Physics-informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions
Majid Rasht-Behesht, Christian Huber, Khemraj Shukla +1
We propose a new approach to the solution of the wave propagation and full waveform inversions (FWIs) based on a recent advance in deep learning called Physics-Informed Neural Netw…
Parallel Physics-Informed Neural Networks via Domain Decomposition
Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis
We develop a distributed framework for the physics-informed neural networks (PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs (XPINNs),…