Results 11 to 20 of about 8,027,948 (326)
Model order reduction assisted by deep neural networks (ROM-net)
Advanced Modeling and Simulation in Engineering Sciences, 2020 In this paper, we propose a general framework for projection-based model order reduction assisted by deep neural networks. The proposed methodology, called ROM-net, consists in using deep learning techniques to adapt the reduced-order model to a ...
Thomas Daniel +3 more
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Model order reduction for gas and energy networks [PDF]
Journal of Mathematics in Industry, 2021 To counter the volatile nature of renewable energy sources, gas networks take a vital role. But, to ensure fulfillment of contracts under these circumstances, a vast number of possible scenarios, incorporating uncertain supply and demand, has to be ...
Christian Himpe +2 more
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Symplectic Model Order Reduction with Non-Orthonormal Bases [PDF]
Mathematical and Computational Applications, 2019 Parametric high-fidelity simulations are of interest for a wide range of applications. However, the restriction of computational resources renders such models to be inapplicable in a real-time context or in multi-query scenarios.
Patrick Buchfink +2 more
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MORLAB—The Model Order Reduction LABoratory [PDF]
Model Reduction of Complex Dynamical Systems, 2021 17 pages, 6 figures, 5 ...
Benner, Peter, Werner, Steffen W. R.
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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs [PDF]
Journal of Computational Physics, 2023 The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real-time and many-query evaluations of parametric
F. Pichi, B. Moya, J. Hesthaven
semanticscholar +1 more source
Model Order Reduction in Neuroscience
, 2020 The human brain contains approximately $10^9$ neurons, each with approximately $10^3$ connections, synapses, with other neurons. Most sensory, cognitive and motor functions of our brains depend on the interaction of a large population of neurons. In recent years, many technologies are developed for recording large numbers of neurons either sequentially
Peter Benner +5 more
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Model order reduction methods for geometrically nonlinear structures: a review of nonlinear techniques [PDF]
Nonlinear dynamics, 2021 This paper aims at reviewing nonlinear methods for model order reduction in structures with geometric nonlinearity, with a special emphasis on the techniques based on invariant manifold theory.
C. Touz'e +2 more
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High order direct parametrisation of invariant manifolds for model order reduction of finite element structures: application to large amplitude vibrations and uncovering of a folding point [PDF]
Nonlinear dynamics, 2021 This paper investigates model-order reduction methods for geometrically nonlinear structures. The parametrisation method of invariant manifolds is used and adapted to the case of mechanical systems in oscillatory form expressed in the physical basis, so ...
Alessandra Vizzaccaro +4 more
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Accelerating Neural ODEs Using Model Order Reduction [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Embedding nonlinear dynamical systems into artificial neural networks is a powerful new formalism for machine learning. By parameterizing ordinary differential equations (ODEs) as neural network layers, these Neural ODEs are memory-efficient to train ...
M. Lehtimäki, L. Paunonen, M. Linne
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Adaptive Data-Driven Model Order Reduction for Unsteady Aerodynamics
Fluids, 2022 A data-driven adaptive reduced order modelling approach is presented for the reconstruction of impulsively started and vortex-dominated flows. A residual-based error metric is presented for the first time in the framework of the adaptive approach.
Peter Nagy, Marco Fossati
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