4 citations · 6 across the 6 of their papers we have counts for
3 papers · 1 filter
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
Vladimir R. Kostic, Karim Lounici, Hélène Halconruy +3
Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decompos…
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
Timothée Devergne, Vladimir Kostic, Michele Parrinello +1
We investigate learning the eigenfunctions of evolution operators for time-reversal invariant stochastic processes, a prime example being the Langevin equation used in molecular dy…
Learning the Infinitesimal Generator of Stochastic Diffusion Processes
Vladimir R. Kostic, Karim Lounici, Helene Halconruy +2
We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems.…