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The increasing size and complexity of modern power systems have led to a high-dimensional mathematical model for transient stability studies, rendering full-scale simulations computationally burdensome. While dimensionality reduction is…

Dynamical Systems · Mathematics 2025-12-08 Farhana Farooq , Danish Rafiq

A non-perturbative approach to the time-averaging of nonlinear, autonomous ODE systems is developed based on invariant manifold methodology. The method is implemented computationally and applied to model problems arising in the mechanics of…

Numerical Analysis · Mathematics 2009-11-11 Amit Acharya , Aarti Sawant

This paper presents the first application of the direct parametrisation method for invariant manifolds to a fully coupled multiphysics problem involving the nonlinear vibrations of deformable structures subjected to an electrostatic field.…

Numerical Analysis · Mathematics 2023-12-25 Attilio Frangi , Alessio Colombo , Alessandra Vizzaccaro , Cyril Touzé

A novel method for the numerical prediction of the slowly varying dynamics of nonlinear mechanical systems has been developed. The method is restricted to the regime of an isolated nonlinear mode and consists of a two-step procedure: In the…

Computational Engineering, Finance, and Science · Computer Science 2021-01-01 Malte Krack , Lars Panning-von Scheidt , Jörg Wallaschek

This work introduces a new approach for accelerating the numerical analysis of time-domain partial differential equations (PDEs) governing complex physical systems. The methodology is based on a combination of a classical reduced-order…

Machine Learning · Computer Science 2024-06-06 Victor Matray , Faisal Amlani , Frédéric Feyel , David Néron

Model order reduction in high-dimensional, nonlinear dynamical systems if often enabled through fast-slow timescale separation. One such approach involves identifying a low-dimensional slow manifold to which the state rapidly converges and…

Dynamical Systems · Mathematics 2026-05-14 Dan Wilson

This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the $\textit{kinematics}$ by approximating the deformation map using an implicit neural representation that…

Machine Learning · Computer Science 2023-02-13 Peter Yichen Chen , Maurizio M. Chiaramonte , Eitan Grinspun , Kevin Carlberg

In many applications, for instance when describing dynamics of fluids or gases, hyperbolic conservation laws arise naturally in the modeling of conserved quantities of a system, like mass or energy. These types of equations exhibit highly…

Numerical Analysis · Mathematics 2022-03-14 Hendrik Kleikamp , Mario Ohlberger , Stephan Rave

Computationally cheap yet accurate dynamical models are a key requirement for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order…

Systems and Control · Electrical Eng. & Systems 2026-02-20 Jan C. Schulze , Alexander Mitsos

Non-intrusive methods have been used since two decades to derive reduced-order models for geometrically nonlinear structures, with a particular emphasis on the so-called STiffness Evaluation Procedure (STEP), relying on the static…

Computational Engineering, Finance, and Science · Computer Science 2022-05-26 Alessandra Vizzaccaro , Arthur Givois , Pierluigi Longobardi , Yichang Shen , Jean-François Deü , Loïc Salles , Cyril Touzé , Olivier Thomas

Dimensionality reduction in mechanical vibratory systems poses challenges for distributed structures including geometric nonlinearities, mainly because of the lack of invariance of the linear subspaces. A reduction method based on direct…

Numerical Analysis · Mathematics 2022-05-26 Andrea Opreni , Alessandra Vizzaccaro , Attilio Frangi , Cyril Touzé

Finite element model updating is a mature discipline for linear structures, yet its extension to nonlinear regimes remains an open challenge. This paper presents a methodology that combines nonlinear model order reduction (NMOR) based on…

Computational Engineering, Finance, and Science · Computer Science 2026-04-09 Nikolaos D. Tantaroudas , Jake Hollins , Konstantinos Agathos , Evangelos Papatheou

While data-driven model reduction techniques are well-established for linearizable mechanical systems, general approaches to reducing non-linearizable systems with multiple coexisting steady states have been unavailable. In this paper, we…

Dynamical Systems · Mathematics 2022-07-13 Mattia Cenedese , Joar Axås , Haocheng Yang , Melih Eriten , George Haller

We provide first the functional analysis background required for reduced order modeling and present the underlying concepts of reduced basis model reduction. The projection-based model reduction framework under affinity assumptions,…

Numerical Analysis · Mathematics 2023-08-30 Gianluigi Rozza , Martin Hess , Giovanni Stabile , Marco Tezzele , Francesco Ballarin

A systematic approach to nonlinear model order reduction (NMOR) of coupled fluid-structureflight dynamics systems of arbitrary fidelity is presented. The technique employs a Taylor series expansion of the nonlinear residual around…

Computational Engineering, Finance, and Science · Computer Science 2026-04-16 Nikolaos D. Tantaroudas , Ilias Karachalios

The usual approach to model reduction for parametric partial differential equations (PDEs) is to construct a linear space $V_n$ which approximates well the solution manifold $\mathcal{M}$ consisting of all solutions $u(y)$ with $y$ the…

Numerical Analysis · Mathematics 2020-05-07 Andrea Bonito , Albert Cohen , Ronald DeVore , Diane Guignard , Peter Jantsch , Guergana Petrova

In this work, we consider wave propagation in materials characterized by nonlinear properties or damage. To accelerate the simulations of the resulting high-dimensional problems, we apply model order reduction methods. Depending on the…

Numerical Analysis · Mathematics 2026-03-24 Saddam Hijazi , Nikiema Fulgence , Hannah Burmester , Natalie Rauter , Carmen Gräßle

In analyzing and assessing the condition of dynamical systems, it is necessary to account for nonlinearity. Recent advances in computation have rendered previously computationally infeasible analyses readily executable on common computer…

Computational Engineering, Finance, and Science · Computer Science 2021-09-24 Thomas Simpson , Nikolaos Dervilis , Eleni Chatzi

In this work, we present a model order reduction technique for nonlinear structures assembled from components.The reduced order model is constructed by reducing the substructures with proper orthogonal decomposition and connecting them by a…

Computational Engineering, Finance, and Science · Computer Science 2024-11-15 Stephan Ritzert , Jannick Kehls , Stefanie Reese , Tim Brepols

For general nonlinear mechanical systems, we derive closed-form, reduced-order models up to cubic order based on rigorous invariant manifold results. For conservative systems, the reduction is based on Lyapunov Subcenter Manifold (LSM)…

Dynamical Systems · Mathematics 2020-01-08 Zsolt Veraszto , Sten Ponsioen , George Haller