A
Andrea Walther
Researcher at University of Paderborn
Publications - 112
Citations - 6027
Andrea Walther is an academic researcher from University of Paderborn. The author has contributed to research in topics: Automatic differentiation & Jacobian matrix and determinant. The author has an hindex of 23, co-authored 109 publications receiving 5497 citations. Previous affiliations of Andrea Walther include Dresden University of Technology & Humboldt University of Berlin.
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Efficient Numerical Solution of Geometric Inverse Problems Involving Maxwell's Equations Using Shape Derivatives and Automatic Code Generation
TL;DR: This work proposes a novel approach using shape derivatives to solve sharp interface geometric inverse optimization problems governed by Maxwell's equations, and describes the underlying formulas and the derivation of appropriate upwind fluxes to arrive at shape gradients for general tracking-type objectives and conservation laws.
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Multiple vector-Jacobian products are cheap
TL;DR: In this paper, the authors examine the relative costs of obtaining various quantities for a linear or nonlinear mapping between Euclidean spaces, assuming that the vector function is evaluated by a computer program as a composition of arithmetic operations and univariate algebraic or transcendental functions such as the square root and the exponential.
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An inverse approach to the characterisation of material parameters of piezoelectric discs with triple-ring-electrodes
Nadine Feldmann,Benjamin Jurgelucks,Leander Claes,Veronika Schulze,Bernd Henning,Andrea Walther +5 more
TL;DR: In this article, a measurement set-up allows to calculate material parameters using one unique disc-shaped specimen with an optimised electrode topology, fitting material parameters can be found using an optimisation procedure.
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A one-shot optimization framework with additional equality constraints applied to multi-objective aerodynamic shape optimization
TL;DR: The extended one-shot approach including additional equality constraints to achieve a direct transition from simulation to optimization to simultaneously achieve optimality and primal as well as adjoint feasibility is concerns.
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Nonsmooth optimization by successive abs-linearization in function spaces
TL;DR: In this article, the authors present and analyze the solution of nonsmooth optimization problems by a quadratic overestimation method in a function space setting under certain assumptions on a suitable local model.