Prof. Carstensen entwickelt mathematische Methoden und Algorithmen zur numerischen Lösung komplexer Differentialgleichungen, die in der Ingenieurpraxis auftreten — insbesondere für Strömungen in porösen Medien, elastische Verformungen und Eigenwertprobleme. Sein aktueller Fokus liegt auf adaptiven Finite-Elemente-Verfahren mit garantierten Fehlerschranken, die es ermöglichen, Rechengitter automatisch zu verfeinern und dabei optimale Konvergenzraten zu erreichen. Diese Methoden reduzieren Rechenzeit und erhöhen die Zuverlässigkeit von Simulationen in der Festkörpermechanik, Strömungsmechanik und Materialwissenschaft. Die Verfahren sind relevant für Industrien, die komplexe physikalische Prozesse simulieren müssen — etwa Werkzeugherstellung, Materialverarbeitung und Konstruktion von Bauteilen unter mechanischer Belastung.
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Prof. Dr. Carsten Carstensen
HU-FIS-Profil ↗Workshop Reliable Methods and Mathematical Modeling
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Förderer: DFG sonstige Programme Zeitraum: 06/2002 - 05/2010 Projektleitung: Prof. Dr. Carsten Carstensen
Zeitraum: 01/2004 - 05/2006 Projektleitung: Prof. Dr. Carsten Carstensen
Förderer: DFG sonstige Programme Zeitraum: 01/2004 - 05/2006 Projektleitung: Prof. Dr. Carsten Carstensen
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · DOI
A mathematical model for a finite–strain elastoplastic evolution problem is proposed in which one time–step of an implicit time–discretization leads to generally non–convex minimization problems. The elimination of all internal variables enables a mathematical and numerical analysis of a reduced problem within the general framework of calculus of variations and nonlinear partial differential equations. The results for a single slip–system and von Mises plasticity illustrate that finite–strain elastoplasticity generates reduced problems with non–quasiconvex energy densities and so allows for non–attainment of energy minimizers and microstructures.
Mathematics of Computation · DOI
A computable error bound for mixed finite element methods is established in the model case of the Poisson–problem to control the error in the H(div, <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="normal upper Omega"> <mml:semantics> <mml:mi mathvariant="normal"> Ω </mml:mi> <mml:annotation encoding="application/x-tex">\Omega</mml:annotation> </mml:semantics> </mml:math> </inline-formula> ) <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="times upper L squared left-parenthesis normal upper Omega right-parenthesis"> <mml:semantics> <mml:mrow> <mml:mo> × </mml:mo> <mml:msup> <mml:mi>L</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo stretchy="false">(</mml:mo> <mml:mi mathvariant="normal"> Ω </mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">\times L^2(\Omega )</mml:annotation> </mml:semantics> </mml:math> </inline-formula> –norm. The reliable and efficient a posteriori error estimate applies, e.g., to Raviart–Thomas, Brezzi-Douglas-Marini, and Brezzi-Douglas-Fortin-Marini elements.
Computers & Mathematics with Applications · DOI
This paper aims first at a simultaneous axiomatic presentation of the proof of optimal convergence rates for adaptive finite element methods and second at some refinements of particular questions like the avoidance of (discrete) lower bounds, inexact solvers, inhomogeneous boundary data, or the use of equivalent error estimators. Solely four axioms guarantee the optimality in terms of the error estimators. Compared to the state of the art in the temporary literature, the improvements of this article can be summarized as follows: First, a general framework is presented which covers the existing literature on optimality of adaptive schemes. The abstract analysis covers linear as well as nonlinear problems and is independent of the underlying finite element or boundary element method. Second, efficiency of the error estimator is neither needed to prove convergence nor quasi-optimal convergence behavior of the error estimator. In this paper, efficiency exclusively characterizes the approximation classes involved in terms of the best-approximation error and data resolution and so the upper bound on the optimal marking parameters does not depend on the efficiency constant. Third, some general quasi-Galerkin orthogonality is not only sufficient, but also necessary for the [Formula: see text]-linear convergence of the error estimator, which is a fundamental ingredient in the current quasi-optimality analysis due to Stevenson 2007. Finally, the general analysis allows for equivalent error estimators and inexact solvers as well as different non-homogeneous and mixed boundary conditions.