Evaluating Artificial Neural Networks and Quantum Computing for Mechanics
Abstract: The popularization of Machine Learning (ML) and the advent of Noisy Intermediate‐Scale Quantum (NISQ) devices for Quantum Computing (QC) sparked new inspiration for the search for techniques reducing computation time in mechanics. We evaluate artificial neural networks (ANNs) as candidates for creating computationally fast surrogate models for otherwise time‐consuming simulations, using a multiscale and multiphase model describing processes in the human liver. We also give a short overview of interesting quantum‐enhanced algorithms capable of reducing computational cost in parts of complex simulations.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Evaluating Artificial Neural Networks and Quantum Computing for Mechanics ; volume:19 ; number:1 ; year:2019 ; extent:2
Proceedings in applied mathematics and mechanics ; 19, Heft 1 (2019) (gesamt 2)
- Creator
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Mielke, André
Ricken, Tim
- DOI
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10.1002/pamm.201900470
- URN
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urn:nbn:de:101:1-2022072208392882501319
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:33 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Mielke, André
- Ricken, Tim