PhD student, Karlsruhe Institute of Technology
3 papers at NeurIPS 2025
We propose a meta-learning approach to enable Graph Network Simulators (GNSs) for fast adaptation to new physical parameters, leveraging conditional neural processes (CNPs) and neural operators to learn shared latent structures.
We iteratively predict target resolutions on intermediate meshes to generate fine-grained adaptive meshes on novel geometries.
We combine denosing diffusion probabilistic models and hierarchical graph neural networks to autoregressively simulate physical dynamics on unstructured meshes.
