42 citations · 80 across the 8 of their papers we have counts for
9 papers
BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training
Kaushik Balakrishnan, Devesh Upadhyay
The task of 2D human pose estimation is challenging as the number of keypoints is typically large (~ 17) and this necessitates the use of robust neural network architectures and tr…
Stochastic Adversarial Koopman Model for Dynamical Systems
Kaushik Balakrishnan, Devesh Upadhyay
Dynamical systems are ubiquitous and are often modeled using a non-linear system of governing equations. Numerical solution procedures for many dynamical systems have existed for s…
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
Kirill Zubov, Zoe McCarthy, Yingbo Ma +11
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical…
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation
Kaushik Balakrishnan, Punarjay Chakravarty, Shubham Shrivastava
Training robots to navigate diverse environments is a challenging problem as it involves the confluence of several different perception tasks such as mapping and localization, foll…
Deep Adversarial Koopman Model for Reaction-Diffusion systems
Kaushik Balakrishnan, Devesh Upadhyay
Reaction-diffusion systems are ubiquitous in nature and in engineering applications, and are often modeled using a non-linear system of governing equations. While robust numerical…
Fluctuating hydrodynamics of multispecies reactive mixtures
A. K. Bhattacharjee, K. Balakrishnan, A. L. Garcia +2
We formulate and study computationally the fluctuating compressible Navier-Stokes equations for reactive multi-species fluid mixtures. We contrast two different expressions for the…