A Bayesian Multiscale Deep Learning Framework for Flows in Random Media. - Govinda Anantha Padmanabha; Nicholas Zabaras (CoRR, 2021)

Solving inverse problems using conditional invertible neural networks. - Govinda Anantha Padmanabha; Nicholas Zabaras (J. Comput. Phys., 2021)

A review on data-driven constitutive laws for solids. - Jan Niklas Fuhg; Govinda Anantha Padmanabha; Nikolaos Bouklas; Bahador Bahmani; WaiChing Sun; Nikolaos N. Vlassis; Moritz Flaschel; Pietro Carrara; Laura De Lorenzis (CoRR, 2024)

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. - Govinda Anantha Padmanabha; Jan Niklas Fuhg; Cosmin Safta; Reese E. Jones; Nikolaos Bouklas (CoRR, 2024)

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks. - Govinda Anantha Padmanabha; Cosmin Safta; Nikolaos Bouklas; Reese E. Jones (CoRR, 2024)

Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials. - Steven Yang; Michal Levin; Govinda Anantha Padmanabha; Miriam Borshevsky; Ohad Cohen; D. Thomas Seidl; Reese E. Jones; Nikolaos Bouklas; Noy Cohen (CoRR, 2025)