41 citations · 302 across the 43 of their papers we have counts for
8 papers · 1 filter
Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds
Patrick Buchfink, Silke Glas, Bernard Haasdonk
Classical model reduction techniques project the governing equations onto linear subspaces of the high-dimensional state-space. For problems with slowly decaying Kolmogorov-n-width…
Surrogate-data-enriched Physics-Aware Neural Networks
Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk +1
Neural networks can be used as surrogates for PDE models. They can be made physics-aware by penalizing underlying equations or the conservation of physical properties in the loss f…
An adaptive model hierarchy for data-augmented training of kernel models for reactive flow
Bernard Haasdonk, Mario Ohlberger, Felix Schindler
We consider machine-learning of time-dependent quantities of interest derived from solution trajectories of parabolic partial differential equations. For large-scale or long-time i…
Analysis of target data-dependent greedy kernel algorithms: Convergence rates for -, - and -greedy
Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
Data-dependent greedy algorithms in kernel spaces are known to provide fast converging interpolants, while being extremely easy to implement and efficient to run. Despite this expe…
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…
A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows
Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev +5
We present an integrated approach for the use of simulated data from full order discretization as well as projection-based Reduced Basis reduced order models for the training of ma…
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Tizian Wenzel, Marius Kurz, Andrea Beck +2
Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that…
Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue
Shahnewaz Shuva, Patrick Buchfink, Oliver Röhrle +1
We present efficient reduced basis (RB) methods for the simulation of the coupled problem consisting of a rigid robot hand interacting with soft tissue material which is modeled by…