9 papers · 1 filter
Adaptive Reduced-Basis Trust-Region Methods for Defect Identification in Elastic Materials
Benedikt Klein, Mario Ohlberger, Thomas Schuster
Monitoring the integrity of elastic structures using ultrasonic waves requires the efficient identification of material parameters from measured surface displacements. The displace…
A Parareal Algorithm with Low-Rank Coarse Solvers
Martin J. Gander, Mario Ohlberger, Stephan Rave
We consider a new class of Parareal algorithms, which use ideas from localized reduced basis methods to construct the coarse solver from truncated SVD approximations of the transfe…
A New Adaptive Deep Learning based Reduced Order Model for Hybrid-Type Parabolic PDEs: Rigorous Error Analysis and Applications
Dawid Kotowski, Mario Ohlberger
This contribution proposes novel data-driven surrogate modeling approaches for parameterized parabolic PDEs, where the parameter dependence can be split into two parts with differe…
Sectional Kolmogorov N-widths for parameter-dependent function spaces: A general framework with application to parametrized Friedrichs' systems
Christian Engwer, Mario Ohlberger, Lukas Renelt
We investigate parametrized variational problems where for each parameter the solution may originate from a different parameter-dependent function space. Our main motivation is the…
Adaptive Model Hierarchies for Multi-Query Scenarios
Hendrik Kleikamp, Mario Ohlberger
In this contribution we present an abstract framework for adaptive model hierarchies together with several instances of hierarchies for specific applications. The hierarchy is part…
A Parareal algorithm without Coarse Propagator?
Martin J. Gander, Mario Ohlberger, Stephan Rave
The Parareal algorithm was invented in 2001 in order to parallelize the solution of evolution problems in the time direction. It is based on parallel fine time propagators called F…