Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties. - Akshay Joshi; Prakash Thakolkaran; Yiwen Zheng; Maxime Escande; Moritz Flaschel; Laura De Lorenzis; Siddhant Kumar (CoRR, 2022)

NN-EUCLID: deep-learning hyperelasticity without stress data. - Prakash Thakolkaran; Akshay Joshi; Yiwen Zheng; Moritz Flaschel; Laura De Lorenzis; Siddhant Kumar (CoRR, 2022)

Artificial neural network supported design of a lattice-based artificial spinal disc for restoring patient-specific anisotropic behaviors. - Zhiyang Yu; Prakash Thakolkaran; Kristina Shea; Tino Stankovic (Comput. Biol. Medicine, 2023)

Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling. - Yiwen Zheng; Prakash Thakolkaran; Jake A. Smith; Ziheng Lu; Shuxin Zheng; Bichlien H. Nguyen; Siddhant Kumar; Aniruddh Vashisth (CoRR, 2023)

Experiment-informed finite-strain inverse design of spinodal metamaterials. - Prakash Thakolkaran; Michael A. Espinal; Somayajulu Dhulipala; Siddhant Kumar; Carlos M. Portela (CoRR, 2023)

Deep learning reveals key predictors of thermal conductivity in covalent organic frameworks. - Prakash Thakolkaran; Yiwen Zheng; Yaqi Guo; Aniruddh Vashisth; Siddhant Kumar (CoRR, 2024)

Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs). - Prakash Thakolkaran; Yaqi Guo; Shivam Saini; Mathias Peirlinck; Benjamin Alheit; Siddhant Kumar (CoRR, 2025)