4 papers
Analysis of Structured Deep Kernel Networks
Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
In this paper, we leverage a recent deep kernel representer theorem to connect kernel based learning and (deep) neural networks in order to understand their interplay. In particula…
Adaptive meshfree approximation for linear elliptic partial differential equations with PDE-greedy kernel methods
Tizian Wenzel, Daniel Winkle, Gabriele Santin +1
We consider meshless approximation for solutions of boundary value problems (BVPs) of elliptic Partial Differential Equations (PDEs) via symmetric kernel collocation. We discuss th…
Data-driven identification of latent port-Hamiltonian systems
Johannes Rettberg, Jonas Kneifl, Julius Herb +3
Conventional physics-based modeling techniques involve high effort, e.g., time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes…
Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems
Robin Herkert, Patrick Buchfink, Bernard Haasdonk +2
Solving high-dimensional dynamical systems in multi-query or real-time applications requires efficient surrogate modelling techniques, as e.g., achieved via model order reduction (…