3 papers
math.NA2026
Regularity-informed data assimilation: A hierarchical Bayesian approach to ensemble Kalman filtering for hyperbolic conservation laws
Jan Glaubitz, Daniel Sharp, Mathieu le Provost +1
We propose a novel regularity-informed filtering framework for data assimilation in the context of hyperbolic conservation laws and other time-dependent partial differential equati…
stat.CO2026
Preserving linear invariants in ensemble filtering methods
Mathieu Le Provost, Jan Glaubitz, Youssef Marzouk
Data assimilation combines dynamical models with observations to improve state estimates. Ensemble filters sequentially assimilate observations by updating a set of samples over ti…
stat.CO2025
A friendly introduction to triangular transport
Maximilian Ramgraber, Daniel Sharp, Mathieu Le Provost +1
Decision making under uncertainty is a cross-cutting challenge in science and engineering. Most approaches to this challenge employ probabilistic representations of uncertainty. In…