3 papers
cs.LG2026
A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants
Nikolaj T. Mücke, Benjamin Sanderse
Bayesian data assimilation combines model forecasts with noisy observations, but sampling high-dimensional, non-Gaussian posteriors remains challenging. We introduce an observation…
cs.CE2025
Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants
Nikolaj T. Mücke, Benjamin Sanderse
Generative models have demonstrated remarkable success in domains such as text, image, and video synthesis. In this work, we explore the application of generative models to fluid d…
math.NA2021
Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications
Nikolaj T. Mücke, Benjamin Sanderse, Sander Bohté +1
In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, Markov Chain Generative Adversarial Neural Networks (MCG…