1.3k citations · 1.6k across the 9 of their papers we have counts for
11 papers
High-Fidelity Description of Platelet Deformation Using a Neural Operator
Marco Laudato, Luca Manzari, Khemraj Shukla
The goal of this work is to investigate the capability of a neural operator (DeepONet) to accurately capture the complex deformation of a platelet's membrane under shear flow. The…
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…
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…
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…