23 citations · 62 across the 12 of their papers we have counts for
12 papers
Local training and enrichment based on a residual localization strategy
Tim Keil, Mario Ohlberger, Felix Schindler +1
To efficiently tackle parametrized multi and/or large scale problems, we propose an adaptive localized model order reduction framework combining both local offline training and loc…
Adaptive Reduced Basis Trust Region Methods for Parameter Identification Problems
Michael Kartmann, Tim Keil, Mario Ohlberger +2
In this contribution, we are concerned with model order reduction in the context of iterative regularization methods for the solution of inverse problems arising from parameter ide…
Adaptive Localized Reduced Basis Methods for Large Scale Parameterized Systems
Tim Keil, Mario Ohlberger, Felix Schindler
In this contribution, we introduce and numerically evaluate a certified and adaptive localized reduced basis method as a local model in a trust-region optimization method for param…
Adaptive Reduced Basis Methods for Multiscale Problems and Large-scale PDE-constrained Optimization
Tim Keil
This thesis presents recent advances in model order reduction methods with the primary aim to construct online-efficient reduced surrogate models for parameterized multiscale pheno…
A Super-Localized Generalized Finite Element Method
Philip Freese, Moritz Hauck, Tim Keil +1
This paper presents a novel multi-scale method for elliptic partial differential equations with arbitrarily rough coefficients. In the spirit of numerical homogenization, the metho…
Adaptive machine learning based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery
Tim Keil, Hendrik Kleikamp, Rolf J Lorentzen +2
In this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we particularly focus on polymer floodi…
A Relaxed Localized Trust-Region Reduced Basis Approach for Optimization of Multiscale Problems
Tim Keil, Mario Ohlberger
In this contribution, we are concerned with parameter optimization problems that are constrained by multiscale PDE state equations. As an efficient numerical solution approach for…
An Online Efficient Two-Scale Reduced Basis Approach for the Localized Orthogonal Decomposition
Tim Keil, Stephan Rave
We are concerned with employing Model Order Reduction (MOR) to efficiently solve parameterized multiscale problems using the Localized Orthogonal Decomposition (LOD) multiscale met…
Model Reduction for Large Scale Systems
Tim Keil, Mario Ohlberger
Projection based model order reduction has become a mature technique for simulation of large classes of parameterized systems. However, several challenges remain for problems where…
An adaptive projected Newton non-conforming dual approach for trust-region reduced basis approximation of PDE-constrained parameter optimization
Stefan Banholzer, Tim Keil, Luca Mechelli +3
In this contribution we device and analyze improved variants of the non-conforming dual approach for trust-region reduced basis (TR-RB) approximation of PDE-constrained parameter o…
A non-conforming dual approach for adaptive Trust-Region Reduced Basis approximation of PDE-constrained optimization
Tim Keil, Luca Mechelli, Mario Ohlberger +2
In this contribution we propose and rigorously analyze new variants of adaptive Trust-Region methods for parameter optimization with PDE constraints and bilateral parameter constra…
Numerical upscaling of perturbed diffusion problems
Fredrik Hellman, Tim Keil, Axel Målqvist
In this paper we study elliptic partial differential equations with rapidly varying diffusion coefficient that can be represented as a perturbation of a reference coefficient. We d…