Efficient neural network models of chemical kinetics using a latent asinh rate transformation-Reference-Cited by-同舟云学术

Efficient neural network models of chemical kinetics using a latent asinh rate transformation

Published:2023 Issue:10 Volume:8 Page:2620-2631
ISSN:2058-9883
Container-title:Reaction Chemistry & Engineering
language:en
Short-container-title:React. Chem. Eng.

Author:

Döppel Felix A.¹^ORCID,Votsmeier Martin¹²

Affiliation:

1. Technical University of Darmstadt, Peter-Grünberg-Straße 8, 64287 Darmstadt, Germany

2. Umicore AG & Co. KG, Rodenbacher Chaussee 4, 63457 Hanau, Germany

Abstract

The proposed latent transformation approach allows building lightweight neural networks that accelerate reactor simulations significantly.

Funder

Bundesministerium für Bildung und Forschung

Publisher

Royal Society of Chemistry (RSC)

Subject

Fluid Flow and Transfer Processes,Process Chemistry and Technology,Chemical Engineering (miscellaneous),Chemistry (miscellaneous),Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2023/RE/D3RE00212H

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