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Model reduction: past, present and future

Guest Editors:
David Néron: Université Paris-Saclay, France
Chinesta Francisco: École nationale supérieure d'Arts et Métiers, France
Pierre Ladevèze: Ecole normale supérieure de Cachan, France

Submission Status: Open   |   Submission Deadline: 31 December 2023


Advanced Modeling and Simulation in Engineering Sciences is calling for submissions to our Collection on Model reduction: past, present and future. Model Order Reduction methods in Computational Mechanics have reached such a degree of maturity that they are becoming indispensable tools in many engineering applications such as simulation or hybrid twins. The reduction in complexity they allow constitutes a considerable advance in many engineering problems that remained practically unsolvable despite the impressive progress in numerical simulation techniques. Unfortunately, their integration into engineering calculation tools is not easy and is thus a current issue. Of course, pushing back the limits of these methods is also an important issue today. What marks the current period and the future is the impact of data science and machine learning methods, which require a thorough re-examination of the issue of complexity reduction. This collection aims to bring together papers that answer all these issues and, more generally, push the limits of the current understanding of Model Order Reduction Methods in Engineering and Mathematics.

Submission Guidelines

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This Collection welcomes submission of Research Articles. 

Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Model reduction: past, present and future" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.

  1. In this work, the space-time MORe DWR (Model Order Reduction with Dual-Weighted Residual error estimates) framework is extended and further developed for single-phase flow problems in porous media. Specifically, ...

    Authors: Hendrik Fischer, Julian Roth, Ludovic Chamoin, Amélie Fau, Mary Wheeler and Thomas Wick
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:9
    Content type: Research article Published on:

  2. We present an accelerated greedy strategy for training of projection-based reduced-order models for parametric steady and unsteady partial differential equations. Our approach exploits hierarchical approximate...

    Authors: Eki Agouzal and Tommaso Taddei
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:8
    Content type: Research article Published on:

  3. The article presents the application of inductive graph machine learning surrogate models for accurate and efficient prediction of 3D flow for industrial geometries, explicitly focusing here on external aerody...

    Authors: Davide Roznowicz, Giovanni Stabile, Nicola Demo, Davide Fransos and Gianluigi Rozza
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:6
    Content type: Research article Published on:

  4. This survey discusses a posteriori error estimation for model order reduction of parametric systems, including linear and nonlinear, time-dependent and steady systems. We focus on introducing the error estimat...

    Authors: Lihong Feng, Sridhar Chellappa and Peter Benner
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:5
    Content type: Research article Published on:

  5. The simulation of magnetic bearings involves highly non-linear physics, with high dependency on the input variation. Moreover, such a simulation is time consuming and can’t run, within realistic computation ti...

    Authors: Chady Ghnatios, Sebastian Rodriguez, Jerome Tomezyk, Yves Dupuis, Joel Mouterde, Joaquim Da Silva and Francisco Chinesta
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:3
    Content type: Research article Published on: