Abstract
ABSTRACT Numerical optimization of complex systems benefits from the technological development of computing platforms in the last 20 years. Unfortunately, this is still not enough, and a large computational time is necessary for the solution of optimization problems when mathematical models that implement rich (and therefore realistic) physical models are adopted. In this paper, we show how the combination of optimization and Artificial Intelligence (AI), in particular Machine Learning algorithms, can help, strongly reducing the overall computational times, making also possible the use of complex simulation systems within the optimization cycle. Original approaches are proposed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
