196 citations · 699 across the 80 of their papers we have counts for
4 papers · 1 filter
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…
Dual-Level Models for Physics-Informed Multi-Step Time Series Forecasting
Mahdi Nasiri, Johanna Kortelainen, Simo Särkkä
This paper develops an approach for multi-step forecasting of dynamical systems by integrating probabilistic input forecasting with physics-informed output prediction. Accurate mul…
Scalable Gaussian Processes for Integrated and Overlapping Measurements Via Augmented State Space Models
Ryan A. Rubenzahl, Soichiro Hattori, Simo Särkkä +4
Astronomical measurements are often integrated over finite exposures, which can obscure latent variability on comparable timescales. Correctly accounting for exposure integration w…