4 papers
Learning PDE Dynamics between Submanifolds Using Green's Observation Operators
Jan Tauberschmidt, Jephte Abijuru, Samuel Okon +5
Many physical systems are driven and observed only on lower-dimensional submanifolds of a larger spatial domain, while their dynamics are governed by the ambient medium occupying t…
Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds
Jan Tauberschmidt, Brian B. Moser, Stanislav Frolov +3
Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in the training data. We instead…
Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
Siwei Ju, Jan Tauberschmidt, Oleg Arenz +2
Learning high-performance control policies that remain consistent with expert behavior is a fundamental challenge in robotics. Reinforcement learning can discover high-performing s…
Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems
Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer +1
We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems. Starting from a model train…