3 citations · 6 across the 14 of their papers we have counts for
14 papers
Robust lane-change intention anticipation under uncertainty based on a recursive Bayesian filtering approach
Dilara Kılınç, Hendrik Kleikamp, Bruno Viti
In this paper we discuss and analyze a method for lane-change intention anticipation of drivers on highways based on kinematic features and surrounding observations. The approach m…
Data-Driven Model Order Reduction with pyMOR
Hendrik Kleikamp, Petar Mlinarić, Stephan Rave +1
pyMOR is a free and open-source software library of model order reduction algorithms for the Python programming language. Designed with classical model-based reduction methods for…
Overcoming slow Kolmogorov width decay in parametric optimal control via neural network surrogates
Hendrik Kleikamp, Martin Lazar, Juan Ricardo Muñoz
In this paper we deal with parametric, linear-quadratic optimal control problems in which the solution can be uniquely characterized by the optimal final time adjoint state. As a m…
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…
Kernel Methods in the Deep Ritz framework: Theory and practice
Hendrik Kleikamp, Tizian Wenzel
In this contribution, kernel approximations are applied as ansatz functions within the Deep Ritz method. This allows to approximate weak solutions of elliptic partial differential…
Two-stage model reduction approaches for the efficient and certified solution of parametrized optimal control problems
Hendrik Kleikamp, Lukas Renelt
In this contribution we develop an efficient reduced order model for solving parametrized linear-quadratic optimal control problems with linear time-varying state system. The fully…
Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics
Björn Schembera, Frank Wübbeling, Hendrik Kleikamp +8
Mathematical models and algorithms are an essential part of mathematical research data, as they are epistemically grounding numerical data. In order to represent models and algorit…
Application of an adaptive model hierarchy to parametrized optimal control problems
Hendrik Kleikamp
In this contribution we apply an adaptive model hierarchy, consisting of a full-order model, a reduced basis reduced order model, and a machine learning surrogate, to parametrized…
Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines
Björn Schembera, Frank Wübbeling, Hendrik Kleikamp +8
In applied mathematics and related disciplines, the modeling-simulation-optimization workflow is a prominent scheme, with mathematical models and numerical algorithms playing a cru…
Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems
Hendrik Kleikamp, Martin Lazar, Cesare Molinari
We consider parametrized linear-quadratic optimal control problems and provide their online-efficient solutions by combining greedy reduced basis methods and machine learning algor…
Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling
Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp +2
In the framework of reduced basis methods, we recently introduced a new certified hierarchical and adaptive surrogate model, which can be used for efficient approximation of input-…
A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs
B. Haasdonk, H. Kleikamp, M. Ohlberger +2
We present a new surrogate modeling technique for efficient approximation of input-output maps governed by parametrized PDEs. The model is hierarchical as it is built on a full ord…
Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain
Hendrik Kleikamp, Mario Ohlberger, Stephan Rave
In many applications, for instance when describing dynamics of fluids or gases, hyperbolic conservation laws arise naturally in the modeling of conserved quantities of a system, li…
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…