1 citations · 2 across the 10 of their papers we have counts for
3 papers · 1 filter
Predicting the formation and stability of oxide perovskites by extracting underlying mechanisms using machine learning
George Stephen Thoppil, Alankar Alankar
The optimization of properties of perovskite oxides has drawn interest on account of their diverse areas of application. In this work, the hierarchical clustering technique is used…
Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials
B V S S Bharadwaja, Mohammad Amin Nabian, Bharatkumar Sharma +2
In this work, a model based on the Physics - Informed Neural Networks (PINNs) for solving elastic deformation of heterogeneous solids and associated Uncertainty Quantification (UQ)…
An Experimentally Informed Continuum Grain Boundary Model
Syed Ansari, Amit Acharya, Alankar Alankar
A continuum grain boundary model is developed that uses experimentally measured grain boundary energy data as a function of misorientation to simulate idealized grain boundary evol…