175 citations · 299 across the 8 of their papers we have counts for
8 papers
Piezoelectric truss metamaterials: data-driven design and additive manufacturing
Saurav Sharma, Satya K. Ammu, Prakash Thakolkaran +3
In the development of active animate materials, electromechanical coupling is highly attractive to realize mechanoresponsive functionality. Piezoelectricity is the most utilized el…
Toward Sustainable Polymer Design: A Molecular Dynamics-Informed Machine Learning Approach for Vitrimers
Yiwen Zheng, Agni K. Biswal, Yaqi Guo +5
Vitrimer is an emerging class of sustainable polymers with self-healing capabilities enabled by dynamic covalent adaptive networks. However, their limited molecular diversity const…
Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)
Prakash Thakolkaran, Yaqi Guo, Shivam Saini +3
Traditional constitutive models rely on hand-crafted parametric forms with limited expressivity and generalizability, while neural network-based models can capture complex material…
Deep learning reveals key predictors of thermal conductivity in covalent organic frameworks
Prakash Thakolkaran, Yiwen Zheng, Yaqi Guo +2
The thermal conductivity of covalent organic frameworks (COFs), an emerging class of nanoporous polymeric materials, is crucial for many applications, yet the link between their st…
Experiment-informed finite-strain inverse design of spinodal metamaterials
Prakash Thakolkaran, Michael A. Espinal, Somayajulu Dhulipala +2
Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when desi…
AI-guided inverse design and discovery of recyclable vitrimeric polymers
Yiwen Zheng, Prakash Thakolkaran, Agni K. Biswal +6
Vitrimer is a new, exciting class of sustainable polymers with the ability to heal due to their dynamic covalent adaptive network that can go through associative rearrangement reac…
NN-EUCLID: deep-learning hyperelasticity without stress data
Prakash Thakolkaran, Akshay Joshi, Yiwen Zheng +3
We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assume…
Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties
Akshay Joshi, Prakash Thakolkaran, Yiwen Zheng +4
Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework fo…