42 citations · 80 across the 8 of their papers we have counts for
3 papers · 1 filter
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…