196 citations · 699 across the 80 of their papers we have counts for
100 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…
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…
Temporal parallelisation of continuous-time maximum-a-posteriori trajectory estimation
Hassan Razavi, Ángel F. García-Fernández, Simo Särkkä
This paper proposes a parallel-in-time method for computing continuous-time maximum-a-posteriori (MAP) trajectory estimates of the states of partially observed stochastic different…
On The Performance of Prefix-Sum Parallel Kalman Filters and Smoothers on GPUs
Simo Särkkä, Ángel F. García-Fernández
This paper presents an experimental evaluation of parallel-in-time Kalman filters and smoothers using graphics processing units (GPUs). In particular, the paper evaluates different…