Publications (186)
Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
Ion Victor Gosea, Luisa Peterson, Pawan Goyal +3
In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This…
A weighted subspace exponential kernel for support tensor machines
Kirandeep Kour, Sergey Dolgov, Peter Benner +2
High-dimensional data in the form of tensors are challenging for kernel classification methods. To both reduce the computational complexity and extract informative features, kernel…
A linear implicit Euler method for the finite element discretization of a controlled stochastic heat equation
Peter Benner, Tony Stillfjord, Christoph Trautwein
We consider a numerical approximation of a linear quadratic control problem constrained by the stochastic heat equation with non-homogeneous Neumann boundary conditions. This invol…
Hankel-Norm Approximation of Large-Scale Descriptor Systems
Peter Benner, Steffen W. R. Werner
The Hankel-norm approximation is a model reduction method which provides the best approximation in the Hankel semi-norm. In this paper the computation of the optimal Hankel-norm ap…
An inexact Newton-Krylov method for stochastic eigenvalue problems
Peter Benner, Akwum Onwunta, Martin Stoll
This paper aims at the efficient numerical solution of stochastic eigenvalue problems. Such problems often lead to prohibitively high dimensional systems with tensor product struct…
Solution decomposition for the nonlinear Poisson-Boltzmann equation using the range-separated tensor format
Cleophas Kweyu, Venera Khoromskaia, Boris Khoromskij +2
The Poisson-Boltzmann equation (PBE) is an implicit solvent continuum model for calculating the electrostatic potential and energies of ionic solvated biomolecules. However, its nu…