4 papers
A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements
Kundan Kumar, Shreya Das, Simo Särkkä
This paper proposes a Bayesian filtering-based approach for learning the dynamics of a physical system from partial, noisy measurements. We model the system dynamics using a Lagran…
Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
Shreya Das, Kundan Kumar, Muhammad Iqbal +4
Model-based reinforcement learning (MBRL) is sample-efficient but depends on the accuracy of the learned dynamics, which are often modeled using black-box methods that do not adher…
Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks
Kundan Kumar, Muhammad Iqbal, Simo Särkkä
Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units. In such scenarios, adversari…
Communication-Efficient Distributed Kalman Filtering using ADMM
Muhammad Iqbal, Kundan Kumar, Simo Särkkä
This paper addresses the problem of optimal linear filtering in a network of local estimators, commonly referred to as distributed Kalman filtering (DKF). The DKF problem is formul…