3 citations · 3 across the 6 of their papers we have counts for
6 papers
KHRONOS: a Kernel-Based Neural Architecture for Rapid, Resource-Efficient Scientific Computation
Reza T. Batley, Sourav Saha
Contemporary models of high dimensional physical systems are constrained by the curse of dimensionality and a reliance on dense data. We introduce KHRONOS (Kernel Expansion Hierarc…
INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics
Taskin Mehereen, Sourav Saha, Intesar Jawad Jaigirdar +1
The ability to accurately model interatomic interactions in large-scale systems is fundamental to understanding a wide range of physical and chemical phenomena, from drug-protein b…
Interpolating neural network: A novel unification of machine learning and interpolation theory
Chanwook Park, Sourav Saha, Jiachen Guo +8
Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, a…
An Introduction to Kernel and Operator Learning Methods for Homogenization by Self-consistent Clustering Analysis
Owen Huang, Sourav Saha, Jiachen Guo +1
Recent advances in operator learning theory have improved our knowledge about learning maps between infinite dimensional spaces. However, for large-scale engineering problems such…
Deep Learning Discrete Calculus (DLDC): A Family of Discrete Numerical Methods by Universal Approximation for STEM Education to Frontier Research
Sourav Saha, Chanwook Park, Stefan Knapik +3
The article proposes formulating and codifying a set of applied numerical methods, coined as Deep Learning Discrete Calculus (DLDC), that uses the knowledge from discrete numerical…
MEAM parameterization for cyclic and tensile deformations of Gold-Silver core-shell systems
Sk Md Ahnaf Akif Alvi, Abrar Faiyad, Md Adnan Mahathir Munshi +3
Gold-Silver (Au-Ag) core-shell nanostructures are gaining importance in stretchable electronics where high tensile and fatigue resistance is of paramount importance. This work prop…