Smart adaptive run parameterization (SArP): enhancement of user manual selection of running parameters in fluid dynamic simulations using bio-inspired and machine-learning techniques

Computational fluid dynamic (CFD) simulations present numerous challenges in the domain of artificial intelligence. Computational time, resources and cost that can reach disproportional size before leading a simulation to its fully converged solution are one of the central issues in this domain. In...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Germany), 2019-11, Vol.23 (22), p.12031-12047
Hauptverfasser:	Ghorbel, Hatem, Zannini, Nicolas, Cherif, Salma, Sauser, Florian, Grunenwald, David, Droz, William, Baradji, Mahamadou, Lakehal, Djamel
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation Artificial Intelligence Computational Intelligence Computing time Control Control algorithms Convergence Domains Engineering Evolutionary algorithms Genetic algorithms Machine learning Mathematical Logic and Foundations Mathematical models Mechatronics Methodologies and Application Methods Numerical analysis Optimization techniques Parameterization Parameters Regression analysis Robotics Simulation Software Turbulence models
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