Model order reduction based on Runge–Kutta neural networks

Model order reduction based on Runge–Kutta neural networks

Model order reduction (MOR) methods enable the generation of real-time-capable digital twins, with the potential to unlock various novel value streams in industry. While traditional projection-based methods are robust and accurate for linear problems, incorporating machine learning to deal with nonl...

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Bibliographic Details
Main Authors: Qinyu Zhuang, Juan Manuel Lorenzi, Hans-Joachim Bungartz, Dirk Hartmann
Format: Article
Language:English
Published: Cambridge University Press 2021-01-01
Series:Data-Centric Engineering
Subjects:
Dynamic parameter sampling
explicit Euler neural network
model order reduction
multilayer perceptron
Runge–Kutta neural network
Online Access:https://www.cambridge.org/core/product/identifier/S2632673621000150/type/journal_article

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https://www.cambridge.org/core/product/identifier/S2632673621000150/type/journal_article

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