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id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.00231">arXiv:2407.00231</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2407.00231">pdf</a>, <a href="https://arxiv.org/format/2407.00231">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> Bridging Large Eddy Simulation and Reduced Order Modeling of Convection-Dominated Flows through Spatial Filtering: Review and Perspectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&amp;query=Quaini%2C+A">Annalisa Quaini</a>, <a href="/search/physics?searchtype=author&amp;query=San%2C+O">Omer San</a>, <a href="/search/physics?searchtype=author&amp;query=Veneziani%2C+A">Alessandro Veneziani</a>, <a href="/search/physics?searchtype=author&amp;query=Iliescu%2C+T">Traian Iliescu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.00231v1-abstract-short" style="display: inline;"> Reduced order models (ROMs) have achieved a lot of success in reducing the computational cost of traditional numerical methods across many disciplines. For convection-dominated (e.g., turbulent) flows, however, standard ROMs generally yield inaccurate results, usually affected by spurious oscillations. Thus, ROMs are usually equipped with numerical stabilization or closure models to account for th&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00231v1-abstract-full').style.display = 'inline'; document.getElementById('2407.00231v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.00231v1-abstract-full" style="display: none;"> Reduced order models (ROMs) have achieved a lot of success in reducing the computational cost of traditional numerical methods across many disciplines. For convection-dominated (e.g., turbulent) flows, however, standard ROMs generally yield inaccurate results, usually affected by spurious oscillations. Thus, ROMs are usually equipped with numerical stabilization or closure models to account for the effect of the discarded modes. The literature on ROM closures and stabilizations is large and growing fast. In this paper, we focus on one particular type of ROM closures and stabilizations that are inspired by Large Eddy Simulation (LES). These ROMs, which we call LES-ROMs, are extremely easy to implement, very efficient, and accurate. Carefully tuned LES-ROMs can accurately capture the average physical quantities of interest in challenging convection-dominated flows in many applications. LES-ROM are constructed by leveraging spatial filtering, i.e., the same principle used to build classical LES models. This ensures a modeling consistency between LES-ROMs and the approaches that generated the data used to train them. It also ``bridges&#39;&#39; two distinct research fields (LES and ROMs), disconnected until now. This paper is a review of LES-ROMs. It starts with a description of a versatile LES strategy called evolve-filter-relax (EFR) that has been successfully used as a full order method. We then show how the EFR strategy, and spatial filtering in general, can be leveraged to construct LES-ROMs. Several applications of LES-ROMs are presented. Finally, we draw conclusions and outline several research directions and open questions in the LES-ROM development. While we do not claim this review to be comprehensive, we certainly hope it serves as a brief and friendly introduction to this exciting research area, which has a lot of potential in practical numerical simulation of convection-dominated flows. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00231v1-abstract-full').style.display = 'none'; document.getElementById('2407.00231v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.12419">arXiv:2205.12419</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2205.12419">pdf</a>, <a href="https://arxiv.org/format/2205.12419">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Pattern Formation and Solitons">nlin.PS</span> </div> </div> <p class="title is-5 mathjax"> Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&amp;query=Ahmed%2C+S+E">Shady E. Ahmed</a>, <a href="/search/physics?searchtype=author&amp;query=San%2C+O">Omer San</a>, <a href="/search/physics?searchtype=author&amp;query=Rasheed%2C+A">Adil Rasheed</a>, <a href="/search/physics?searchtype=author&amp;query=Iliescu%2C+T">Traian Iliescu</a>, <a href="/search/physics?searchtype=author&amp;query=Veneziani%2C+A">Alessandro Veneziani</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.12419v1-abstract-short" style="display: inline;"> We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost. The hierarchical structure of the ROM basis and the VMS framework enable a natural separation of the resolved and unresolved ROM spatial scales. Modern PGML&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12419v1-abstract-full').style.display = 'inline'; document.getElementById('2205.12419v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.12419v1-abstract-full" style="display: none;"> We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost. The hierarchical structure of the ROM basis and the VMS framework enable a natural separation of the resolved and unresolved ROM spatial scales. Modern PGML algorithms are used to construct novel models for the interaction among the resolved and unresolved ROM scales. Specifically, the new framework builds ROM operators that are closest to the true interaction terms in the VMS framework. Finally, machine learning is used to reduce the projection error and further increase the ROM accuracy. Our numerical experiments for a two-dimensional vorticity transport problem show that the novel PGML-VMS-ROM paradigm maintains the low computational cost of current ROMs, while significantly increasing the ROM accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12419v1-abstract-full').style.display = 'none'; document.getElementById('2205.12419v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.05320">arXiv:2005.05320</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/2005.05320">pdf</a>, <a href="https://arxiv.org/format/2005.05320">other</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> Simulating the spread of COVID-19 via spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&amp;query=Viguerie%2C+A">Alex Viguerie</a>, <a href="/search/physics?searchtype=author&amp;query=Lorenzo%2C+G">Guillermo Lorenzo</a>, <a href="/search/physics?searchtype=author&amp;query=Auricchio%2C+F">Ferdinando Auricchio</a>, <a href="/search/physics?searchtype=author&amp;query=Baroli%2C+D">Davide Baroli</a>, <a href="/search/physics?searchtype=author&amp;query=Hughes%2C+T+J+R">Thomas J. R. Hughes</a>, <a href="/search/physics?searchtype=author&amp;query=Patton%2C+A">Alessia Patton</a>, <a href="/search/physics?searchtype=author&amp;query=Reali%2C+A">Alessandro Reali</a>, <a href="/search/physics?searchtype=author&amp;query=Yankeelov%2C+T+E">Thomas E. Yankeelov</a>, <a href="/search/physics?searchtype=author&amp;query=Veneziani%2C+A">Alessandro Veneziani</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.05320v1-abstract-short" style="display: inline;"> We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generate&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.05320v1-abstract-full').style.display = 'inline'; document.getElementById('2005.05320v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.05320v1-abstract-full" style="display: none;"> We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level. Additional simulations exploring alternative scenarios for the relaxation of lockdown restrictions suggest that reopening strategies should account for local population densities and the specific dynamics of the contagion. Thus, we argue that data-driven simulations of our model could ultimately inform health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.05320v1-abstract-full').style.display = 'none'; document.getElementById('2005.05320v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 3 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> G.1.8; G.1.10 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.04535">arXiv:1803.04535</a> <span>&nbsp;[<a href="https://arxiv.org/pdf/1803.04535">pdf</a>]&nbsp;</span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</span> </div> </div> <p class="title is-5 mathjax"> Computational methods in cardiovascular mechanics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&amp;query=Auricchio%2C+F">Ferdinando Auricchio</a>, <a href="/search/physics?searchtype=author&amp;query=Conti%2C+M">Michele Conti</a>, <a href="/search/physics?searchtype=author&amp;query=Lefieux%2C+A">Adrian Lefieux</a>, <a href="/search/physics?searchtype=author&amp;query=Morganti%2C+S">Simone Morganti</a>, <a href="/search/physics?searchtype=author&amp;query=Reali%2C+A">Alessandro Reali</a>, <a href="/search/physics?searchtype=author&amp;query=Rozza%2C+G">Gianluigi Rozza</a>, <a href="/search/physics?searchtype=author&amp;query=Veneziani%2C+A">Alessandro Veneziani</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.04535v1-abstract-short" style="display: inline;"> The introduction of computational models in cardiovascular sciences has been progressively bringing new and unique tools for the investigation of the physiopathology. Together with the dramatic improvement of imaging and measuring devices on one side, and of computational architectures on the other one, mathematical and numerical models have provided a new, clearly noninvasive, approach for unders&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.04535v1-abstract-full').style.display = 'inline'; document.getElementById('1803.04535v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.04535v1-abstract-full" style="display: none;"> The introduction of computational models in cardiovascular sciences has been progressively bringing new and unique tools for the investigation of the physiopathology. Together with the dramatic improvement of imaging and measuring devices on one side, and of computational architectures on the other one, mathematical and numerical models have provided a new, clearly noninvasive, approach for understanding not only basic mechanisms but also patient-specific conditions, and for supporting the design and the development of new therapeutic options. The terminology in silico is, nowadays, commonly accepted for indicating this new source of knowledge added to traditional in vitro and in vivo investigations. The advantages of in silico methodologies are basically the low cost in terms of infrastructures and facilities, the reduced invasiveness and, in general, the intrinsic predictive capabilities based on the use of mathematical models. The disadvantages are generally identified in the distance between the real cases and their virtual counterpart required by the conceptual modeling that can be detrimental for the reliability of numerical simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.04535v1-abstract-full').style.display = 'none'; document.getElementById('1803.04535v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">54 pages, Book Chapter</span> </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <span class="help" style="display: inline-block;"><a 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