REDUCED-ORDER MODELLING OF PARAMETERIZED TRANSIENT FLOWS IN CLOSED-LOOP SYSTEMS [PDF]
EPJ Web of Conferences, 2021 In this paper, two Galerkin projection based reduced basis approaches are investigated for the reduced-order modeling of parameterized incompressible Navier-Stokes equations for laminar transient flows. The first approach solves only the reduced momentum
German Péter +3 more
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Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions [PDF]
Computer Methods in Applied Mechanics and Engineering, 2022 Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, which might become prohibitive when approximating steady-state solutions of ...
Paolo Conti +4 more
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Parametric Dynamic Mode Decomposition for Reduced Order Modeling [PDF]
Journal of Computational Physics, 2022 Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. The DMD low-rank or reduced operator is typically obtained by singular value
Quincy A. Huhn +3 more
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A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling [PDF]
Computer Methods in Applied Mechanics and Engineering, 2021 The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems.
A. Gruber +3 more
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Reduced Order Modeling Using Advection-Aware Autoencoders
Mathematical and Computational Applications, 2022 Physical systems governed by advection-dominated partial differential equations (PDEs) are found in applications ranging from engineering design to weather forecasting.
Sourav Dutta +3 more
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Nuclear science and engineering, 2022 This paper proposes an approach that combines reduced-order models with machine learning in order to create physics-informed digital twins to predict high-dimensional output quantities of interest, such as neutron flux and power distributions in nuclear ...
Helin Gong +3 more
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Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques [PDF]
Advances in Water Resources, 2021 Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of $\mathrm{CO_2}$ sequestration).
T. Kadeethum +5 more
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Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations [PDF]
Chaos, 2021 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 finding a coordinate
Alec J. Linot, M. Graham
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Component-Based Reduced Order Modeling of Large-Scale Complex Systems
Frontiers in Physics, 2022 Large-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena.
Cheng Huang +2 more
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A Comprehensive Deep Learning-Based Approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs [PDF]
Journal of Scientific Computing, 2020 Conventional reduced order modeling techniques such as the reduced basis (RB) method (relying, e.g., on proper orthogonal decomposition (POD)) may incur in severe limitations when dealing with nonlinear time-dependent parametrized PDEs, as these are ...
S. Fresca, L. Dede’, A. Manzoni
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