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
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
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
Mielke, André
Ricken, Tim

DOI
10.1002/pamm.201900470
URN
urn:nbn:de:101:1-2022072208392882501319
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:33 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Associated

  • Mielke, André
  • Ricken, Tim

Other Objects (12)