2021
DOI: 10.48550/arxiv.2104.01914
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Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows

Abstract: Chemically reacting flows are common in engineering, such as hypersonic flow, combustion, explosions, manufacturing processes and environmental assessments. For combustion, the number of reactions can be significant (over 100) and due to the very large CPU requirements of chemical reactions (over 99%) a large number of flow and combustion problems are presently beyond the capabilities of even the largest supercomputers. Motivated by this, novel Deep Neural Networks (DNNs) are introduced to approximate stiff OD… Show more

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Cited by 1 publication
(2 citation statements)
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“…DNNs are known to learn ODEs/PDEs [5,16,25,30,40]. In case of stiff or chaotic systems [16], one idea is to learn one time step map, i.e., learn the update map u n−1 → u n .…”
mentioning
confidence: 99%
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“…DNNs are known to learn ODEs/PDEs [5,16,25,30,40]. In case of stiff or chaotic systems [16], one idea is to learn one time step map, i.e., learn the update map u n−1 → u n .…”
mentioning
confidence: 99%
“…DNNs are known to learn ODEs/PDEs [5,16,25,30,40]. In case of stiff or chaotic systems [16], one idea is to learn one time step map, i.e., learn the update map u n−1 → u n . The neural net takes as input the state at time t n−1 and the output is the approximate state at time t n .…”
mentioning
confidence: 99%