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2107.02597
Cited By

Physics-informed regularization and structure preservation for learning
stable reduced models from data with operator inference

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference

6 July 2021

Benjamin Peherstorfer

ArXiv (abs)PDF HTML

Papers citing "Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference"

8 / 8 papers shown

Physics-Informed Machine Learning for Characterizing System Stability

Physics-Informed Machine Learning for Characterizing System Stability

137

0

0

11 Nov 2025

Nested Operator Inference for Adaptive Data-Driven Learning of Reduced-order Models

Nested Operator Inference for Adaptive Data-Driven Learning of Reduced-order Models

195

2

0

15 Aug 2025

Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction

Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction

Paul Schwerdtner

M. H. D. Frahan

Benjamin Peherstorfer

175

1

0

28 Jul 2025

Data-Driven Discovery of PDEs via the Adjoint Method

Data-Driven Discovery of PDEs via the Adjoint Method

Kamal Youcef-Toumi

537

1

0

30 Jan 2024

Nonlinear embeddings for conserving Hamiltonians and other quantities
with Neural Galerkin schemes

Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemesSIAM Journal on Scientific Computing (SISC), 2023

Paul Schwerdtner

Philipp Schulze

Benjamin Peherstorfer

249

12

0

11 Oct 2023

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

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

230

27

0

26 Aug 2023

Inference of Continuous Linear Systems from Data with Guaranteed
Stability

Inference of Continuous Linear Systems from Data with Guaranteed Stability

207

5

0

24 Jan 2023

Operator inference with roll outs for learning reduced models from
scarce and low-quality data

Operator inference with roll outs for learning reduced models from scarce and low-quality dataComputers and Mathematics with Applications (CMA), 2022

Benjamin Peherstorfer

302

23

0

02 Dec 2022

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