22 citations · 30 across the 8 of their papers we have counts for
8 papers
Finite-width adiabatic shear banding and dislocation patterning in mesoscale polycrystalline aggregates
Siddharth Singh, Rajat Arora, Janith Wanni +6
Dynamic shear banding under adiabatic conditions in a mesoscale polycrystalline aggregate is studied using a model of mesoscale dislocation mechanics and experiments. The model inv…
A Deep Learning Framework for Solving Hyperbolic Partial Differential Equations: Part I
Rajat Arora
Physics informed neural networks (PINNs) have emerged as a powerful tool to provide robust and accurate approximations of solutions to partial differential equations (PDEs). Howeve…
CapsFlow: Optical Flow Estimation with Capsule Networks
Rahul Chand, Rajat Arora, K Ram Prabhakar +1
We present a framework to use recently introduced Capsule Networks for solving the problem of Optical Flow, one of the fundamental computer vision tasks. Most of the existing state…
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning
Rajat Arora, Ankit Shrivastava
This work presents a physics-informed deep learning-based super-resolution framework to enhance the spatio-temporal resolution of the solution of time-dependent partial differentia…
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics
Rajat Arora
Traditional approaches based on finite element analyses have been successfully used to predict the macro-scale behavior of heterogeneous materials (composites, multicomponent alloy…
Mechanics of micropillar confined thin film plasticity
Abhishek Arora, Rajat Arora, Amit Acharya
Micropillar compression experiments probing size effects in confined plasticity of metal thin films, including the indirect imposition of 'canonical' simple shearing boundary condi…
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
Rajat Arora, Pratik Kakkar, Biswadip Dey +1
This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic so…
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity
Rajat Arora
This work presents a novel physics-informed deep learning based super-resolution framework to reconstruct high-resolution deformation fields from low-resolution counterparts, obtai…