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Out-of-equilibrium quantum many-body systems exhibit rapid correlation buildup that underlies many emerging phenomena. Exact wave-function methods to describe this scale exponentially with particle number; simpler mean-field approaches…

Machine Learning · Computer Science 2026-05-06 Patrick Egenlauf , Iva Březinová , Sabine Andergassen , Miriam Klopotek

The treatment of Hall-effect thrusters as nonlinear, dynamical systems has emerged as a new perspective to understand and analyze data acquired from the thrusters. The acquisition of high-speed data that can resolve the characteristic…

Chaotic Dynamics · Physics 2024-08-14 Adrian S Wong , Christine M Greve , Daniel Q Eckhardt

Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation into the finite-dimensional algebraic system solved by computers. Due to complicated nature of the…

Computational Physics · Physics 2021-07-23 Luning Sun , Han Gao , Shaowu Pan , Jian-Xun Wang

The current study aims to evaluate and investigate the development of projection-based reduced-order models (ROMs) for efficient and accurate RDE simulations. Specifically, we focus on assessing the projection-based ROM construction…

Fluid Dynamics · Physics 2024-08-29 Ryan Camacho , Cheng Huang

In recent years, increasingly complex computational models are being built to describe physical systems which has led to increased use of surrogate models to reduce computational cost. In problems related to Structural Health Monitoring…

Machine Learning · Computer Science 2024-07-08 Nicholas E. Silionis , Theodora Liangou , Konstantinos N. Anyfantis

Multiscale problems are widely observed across diverse domains in physics and engineering. Translating these problems into numerical simulations and solving them using numerical schemes, e.g. the finite element method, is costly due to the…

Recent work in deep learning focuses on solving physical systems in the Ordinary Differential Equation or Partial Differential Equation. This current work proposed a variant of Convolutional Neural Networks (CNNs) that can learn the hidden…

Machine Learning · Computer Science 2021-11-02 Mansura Habiba , Barak A. Pearlmutter

Flexible spacecraft structures present significant challenges for physical and control system design due to nonlinear dynamics, mission constraints, environmental variables, and changing operational conditions. This paper presents a…

Systems and Control · Electrical Eng. & Systems 2026-01-13 Matthew Hilsenrath , Daniel R. Herber

Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to attractors that exist on finite-dimensional manifolds. We present a data-driven reduced order modeling method that capitalizes on this fact by…

Machine Learning · Computer Science 2022-07-20 Alec J. Linot , Michael D. Graham

The computational resources required to solve the full 3D inversion of time-domain electromagnetic data are immense. To overcome the time-consuming 3D simulations, we construct a surrogate model, more precisely, a data-driven statistical…

Geophysics · Physics 2024-07-10 Wouter Deleersnyder , David Dudal , Thomas Hermans

Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flow and transport with geochemical reactions is…

Computational Engineering, Finance, and Science · Computer Science 2025-04-28 Vinicius L S Silva , Geraldine Regnier , Pablo Salinas , Claire E Heaney , Matthew D Jackson , Christopher C Pain

Kinetics of dislocations is studied by means of computer simulation during intensive plastic deformation. The dynamical effect in the form of soliton-like wave of sharply disrupted interparticle bonds is observed. Along with it, micropores…

Materials Science · Physics 2007-05-23 L. S. Metlov

Oilfield development related decisions are made using reservoir simulation-based optimization study in which different production scenarios and well controls are compared. Such simulations are computationally expensive and so surrogate…

Machine Learning · Computer Science 2022-03-01 Ajitabh Kumar

Numerical simulation of ordinary differential equations (ODEs) can be challenging when the system exhibits high accelerations and rapidly changing dynamics. Under these conditions the ODE solver often needs to take very small time steps in…

Numerical Analysis · Mathematics 2026-05-11 Andrew Tagg , Andrew Frandsen , Andrew Ning

Modal synthesis methods are a long-standing approach for modelling distributed musical systems. In some cases extensions are possible in order to handle geometric nonlinearities. One such case is the high-amplitude vibration of a string,…

Sound · Computer Science 2025-05-16 Victor Zheleznov , Stefan Bilbao , Alec Wright , Simon King

High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated for modeling a given…

Machine Learning · Computer Science 2023-09-04 Paolo Conti , Mengwu Guo , Andrea Manzoni , Attilio Frangi , Steven L. Brunton , J. Nathan Kutz

Neural networks have emerged as powerful surrogates for solving partial differential equations (PDEs), offering significant computational speedups over traditional methods. However, these models suffer from a critical limitation: error…

Machine Learning · Computer Science 2025-12-29 Xinquan Huang , Paris Perdikaris

Complex envelope and reduced phase simulation models describing the dynamical behavior of an optoelectronic oscillator (OEO) under injection by an external source are described. The models are built on the foundations of a previously…

Optics · Physics 2023-09-26 A. Banerjee , T. J. Hall

Surrogate modeling and active subspaces have emerged as powerful paradigms in computational science and engineering. Porting such techniques to computational models in the social sciences brings into sharp relief their limitations in…

Machine Learning · Statistics 2023-10-19 Nathan Wycoff

In recent years, there has been an increasing need for Nuclear Power Plants (NPPs) to improve flexibility in order to match the rapid growth of renewable energies. The Operator Assistance Predictive System (OAPS) developed by Framatome…

Machine Learning · Computer Science 2026-03-26 Perceval Beja-Battais , Alain Grossetête , Nicolas Vayatis
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