645 citations · 2.1k across the 59 of their papers we have counts for
9 papers · 1 filter
The Graph Theoretic Approach for Nodal Cross Section Parameterization
Brendan Kochunas, Krishna Garikipati, Matthew Duschenes +1
Presently, models for the parameterization of cross sections for nodal diffusion nuclear reactor calculations at different conditions using histories and branches are developed fro…
A Perspective on Regression and Bayesian Approaches for System Identification of Pattern Formation Dynamics
Zhenlin Wang, Bowei Wu, Krishna Garikipati +1
We present two approaches to system identification, i.e. the identification of partial differential equations (PDEs) from measurement data. The first is a regression-based Variatio…
Machine learning materials physics: Deep neural networks trained on elastic free energy data from martensitic microstructures predict homogenized stress fields with high accuracy
K. Sagiyama, K. Garikipati
We present an approach to numerical homogenization of the elastic response of microstructures. Our work uses deep neural network representations trained on data obtained from direc…
Variational system identification of the partial differential equations governing pattern-forming physics: Inference under varying fidelity and noise
Zhenlin Wang, Xun Huan, Krishna Garikipati
We present a contribution to the field of system identification of partial differential equations (PDEs), with emphasis on discerning between competing mathematical models of patte…
Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
Gregory Teichert, Krishna Garikipati
Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recomme…
A multi-physics battery model with particle scale resolution of porosity evolution driven by intercalation strain and electrolyte flow
Zhenlin Wang, Krishna Garikipati
We present a coupled continuum formulation for the electrostatic, chemical, thermal, mechanical and fluid physics in battery materials. Our treatment is at the particle scale, at w…