b'Peter Benner'

research

∙ 09/13/2023

A Robust SINDy Approach by Combining Neural Networks and an Integral Form

The discovery of governing equations from data has been an active field ...

0 Ali Forootani, et al. ∙

research

∙ 08/26/2023

Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics

Discovering a suitable coordinate transformation for nonlinear systems e...

0 Pawan Goyal, et al. ∙

research

∙ 08/26/2023

Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference

Scientific machine learning for learning dynamical systems is a powerful...

0 Pawan Goyal, et al. ∙

research

∙ 08/07/2023

Parameterized Interpolation of Passive Systems

We study the tangential interpolation problem for a passive transfer fun...

0 Peter Benner, et al. ∙

research

∙ 08/04/2023

Linearly Implicit Global Energy Preserving Reduced-order Models for Cubic Hamiltonian Systems

This work discusses the model reduction problem for large-scale multi-sy...

0 Süleyman Yıldız, et al. ∙

research

∙ 08/02/2023

Data-Driven Identification of Quadratic Symplectic Representations of Nonlinear Hamiltonian Systems

We present a framework for learning Hamiltonian systems using data. This...

0 Süleyman Yıldız, et al. ∙

research

∙ 07/20/2023

Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure

Accurate error estimation is crucial in model order reduction, both to o...

0 Sridhar Chellappa, et al. ∙

research

∙ 06/30/2023

Towards a Benchmark Framework for Model Order Reduction in the Mathematical Research Data Initiative (MaRDI)

The race for the most efficient, accurate, and universal algorithm in sc...

0 Peter Benner, et al. ∙

research

∙ 06/09/2023

Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems

When repeated evaluations for varying parameter configurations of a high...

0 Harshit Kapadia, et al. ∙

research

∙ 05/10/2023

Parametric Dynamic Mode Decomposition for nonlinear parametric dynamical systems

A non-intrusive model order reduction (MOR) method that combines feature...

0 Shuwen Sun, et al. ∙

research

∙ 04/27/2023

Structured interpolation for multivariate transfer functions of quadratic-bilinear systems

High-dimensional/high-fidelity nonlinear dynamical systems appear natura...

0 Peter Benner, et al. ∙

research

∙ 02/19/2023

Rank-Minimizing and Structured Model Inference

While extracting information from data with machine learning plays an in...

0 Pawan Goyal, et al. ∙

research

∙ 02/16/2023

A weighted subspace exponential kernel for support tensor machines

High-dimensional data in the form of tensors are challenging for kernel ...

0 Kirandeep Kour, et al. ∙

research

∙ 01/24/2023

Inference of Continuous Linear Systems from Data with Guaranteed Stability

Machine-learning technologies for learning dynamical systems from data p...

0 Pawan Goyal, et al. ∙

research

∙ 01/23/2023

Dominant Subspaces of High-Fidelity Nonlinear Structured Parametric Dynamical Systems and Model Reduction

In this work, we investigate a model order reduction scheme for high-fid...

0 Pawan Goyal, et al. ∙

research

∙ 01/13/2023

Multi-fidelity error estimation accelerates greedy model reduction of complex dynamical systems

Model order reduction usually consists of two stages: the offline stage ...

0 Lihong Feng, et al. ∙

research

∙ 11/01/2022

Generalized Quadratic-Embeddings for Nonlinear Dynamics using Deep Learning

The engineering design process (e.g., control and forecasting) relies on...

0 Pawan Goyal, et al. ∙

research

∙ 09/12/2022

Riccati-feedback Control of a Two-dimensional Two-phase Stefan Problem

We discuss the feedback control problem for a two-dimensional two-phase ...

0 Björn Baran, et al. ∙

research

∙ 06/03/2022

A unifying framework for tangential interpolation of structured bilinear control systems

In this paper, we consider the structure-preserving model order reductio...

0 Peter Benner, et al. ∙

research

∙ 05/19/2022

Neural ODEs with Irregular and Noisy Data

Measurement noise is an integral part while collecting data of a physica...

1 Pawan Goyal, et al. ∙

research

∙ 03/31/2022

Krylov techniques for low-rank ADI

One of the most computationally expensive steps of the low-rank ADI meth...

0 Peter Benner, et al. ∙

research

∙ 03/16/2022

A Structure-Preserving Divide-and-Conquer Method for Pseudosymmetric Matrices

We devise a spectral divide-and-conquer scheme for matrices that are sel...

0 Peter Benner, et al. ∙

research

∙ 11/25/2021

Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learning

Learning dynamical models from data plays a vital role in engineering de...

0 Pawan Goyal, et al. ∙

research

∙ 11/12/2021

A low-rank solution method for Riccati equations with indefinite quadratic terms

Algebraic Riccati equations with indefinite quadratic terms play an impo...

0 Peter Benner, et al. ∙

research

∙ 10/12/2021

Fast A Posteriori State Error Estimation for Reliable Frequency Sweeping in Microwave Circuits via the Reduced-Basis Method

We develop a compact, reliable model order reduction approach for fast f...

0 Valentin de la Rubia, et al. ∙

research

∙ 10/07/2021

Palindromic linearization and numerical solution of nonsymmetric algebraic T-Riccati equations

We identify a relationship between the solutions of a nonsymmetric algeb...

0 Peter Benner, et al. ∙

research

∙ 09/28/2021

Solution decomposition for the nonlinear Poisson-Boltzmann equation using the range-separated tensor format

The Poisson-Boltzmann equation (PBE) is an implicit solvent continuum mo...

0 Cleophas Kweyu, et al. ∙

research

∙ 09/23/2021

Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint

Measurement noise is an integral part while collecting data of a physica...

79 Pawan Goyal, et al. ∙

research

∙ 09/13/2021

Learning reduced order models from data for hyperbolic PDEs

Given a set of solution snapshots of a hyperbolic PDE, we are interested...

0 Neeraj Sarna, et al. ∙

research

∙ 08/05/2021

Next-Gen Gas Network Simulation

To overcome many-query optimization, control, or uncertainty quantificat...

0 Christian Himpe, et al. ∙

research

∙ 05/11/2021

Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach

Discovering dynamical models to describe underlying dynamical behavior i...

0 Pawan Goyal, et al. ∙

research

∙ 05/02/2021

Data-Driven Model Order Reduction for Problems with Parameter-Dependent Jump-Discontinuities

We propose a data-driven model order reduction (MOR) technique for param...

0 Neeraj Sarna, et al. ∙

research

∙ 04/26/2021

Inf-Sup-Constant-Free State Error Estimator for Model Order Reduction of Parametric Systems in Electromagnetics

A reliable model order reduction process for parametric analysis in elec...

0 Sridhar Chellappa, et al. ∙

research

∙ 04/14/2021

Stable and Efficient Computation of Generalized Polar Decompositions

We present methods for computing the generalized polar decomposition of ...

0 Peter Benner, et al. ∙

research

∙ 03/10/2021

A Training Set Subsampling Strategy for the Reduced Basis Method

We present a subsampling strategy for the offline stage of the Reduced B...

0 Sridhar Chellappa, et al. ∙

research

∙ 03/03/2021

LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes

Mathematical modeling is an essential step, for example, to analyze the ...

0 Pawan Goyal, et al. ∙

research

∙ 02/27/2021

Reduced basis method for the nonlinear Poisson-Boltzmann equation regularized by the range-separated canonical tensor format

The Poisson-Boltzmann equation (PBE) is a fundamental implicit solvent c...

0 Cleophas Kweyu, et al. ∙

research

∙ 11/24/2020

Model Order Reduction for Gas and Energy Networks

To counter the volatile nature of renewable energy sources, gas networks...

0 Christian Himpe, et al. ∙

research

∙ 11/12/2020

Parallel Algorithms for Tensor Train Arithmetic

We present efficient and scalable parallel algorithms for performing mat...

0 Hussam Al Daas, et al. ∙

research

∙ 10/13/2020

Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flows

Reduced-order modeling has a long tradition in computational fluid dynam...

0 Peter Benner, et al. ∙

research

∙ 10/13/2020

Structure-Preserving Model Reduction for Dissipative Mechanical Systems

Suppressing vibrations in mechanical models, usually described by second...

0 Rebekka S. Beddig, et al. ∙

research

∙ 10/12/2020

Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties

Currently, the growth of material data from experiments and simulations ...

0 Anke Stoll, et al. ∙

research

∙ 09/17/2020

Data-Driven Snapshot Calibration via Monotonic Feature Matching

Snapshot matrices of hyperbolic equations have a slow singular value dec...

0 Neeraj Sarna, et al. ∙

research

∙ 09/03/2020

A rational Even-IRA algorithm for the solution of T-even polynomial eigenvalue problems

In this work we present a rational Krylov subspace method for solving re...

0 Peter Benner, et al. ∙

research

∙ 09/02/2020

Space and Chaos-Expansion Galerkin POD Low-order Discretization of PDEs for Uncertainty Quantification

The quantification of multivariate uncertainties in partial differential...

0 Peter Benner, et al. ∙

research

∙ 08/24/2020

A Low-rank Method for Parameter-dependent Fluid-structure Interaction Discretizations With Hyperelasticity

In aerospace engineering and boat building, fluid-structure interaction ...

0 Roman Weinhandl, et al. ∙

research

∙ 08/20/2020

Efficient and Accurate Algorithms for Solving the Bethe-Salpeter Eigenvalue Problem for Crystalline Systems

Optical properties of materials related to light absorption and scatteri...

0 Peter Benner, et al. ∙

research

∙ 07/28/2020

Data-Driven Learning of Reduced-order Dynamics for a Parametrized Shallow Water Equation

This paper discusses a non-intrusive data-driven model order reduction m...

0 Süleyman Yıldız, et al. ∙

research

∙ 07/22/2020

Structure-Preserving Interpolation for Model Reduction of Parametric Bilinear Systems

In this paper, we present an interpolation framework for structure-prese...

0 Peter Benner, et al. ∙

research

∙ 06/11/2020

GR decompositions and their relations to Cholesky-like factorizations

For a given matrix, we are interested in computing GR decompositions A=G...

0 Peter Benner, et al. ∙

Peter Benner

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