Article

Artificial Intelligence in Modeling and Simulation

Algorithms

Fachada, Nuno; David, Nuno2024MDPI

Key information

Authors:

Fachada, Nuno (Nuno Fachada); David, Nuno

Published in

June 15, 2024

Abstract

Modeling and simulation (M&S) serve as essential tools in various scientific and engineering domains, enabling the representation of complex systems and processes without the constraints of physical experimentation. These tools have evolved significantly with the integration of artificial intelligence (AI), which offers advanced capabilities in essential aspects of M&S such as optimization, data analysis, and verification and validation. AI’s capacity to enhance M&S is demonstrated in applications ranging from engineering and physics to social sciences and biology, providing novel approaches to problem-solving and system understanding.

Publication details

Authors in the community:

Publication version

VoR - Version of Record

Publisher

MDPI

Link to the publisher's version

https://www.mdpi.com/1999-4893/17/6/265

Title of the publication container

Algorithms

First page or article number

265

Volume

17

Issue

6

ISSN

1999-4893

Fields of Science and Technology (FOS)

computer-and-information-sciences - Computer and information sciences

Keywords

  • modeling and simulation
  • artificial intelligence
  • machine learning
  • verification and validation
  • metamodeling

Publication language (ISO code)

eng - English

Rights type:

Open access

Creative Commons license

CC-BY - Attribution

Financing entity

Fundação para a Ciência e a Tecnologia

Visit project

Identifier for the funding entity: http://dx.doi.org/10.13039/501100001871

Type of identifier of the funding entity: Crossref Funder

Number for the project, award or grant: CEECINST/00002/2021/CP2788/CT0001

Financing entity

Fundação para a Ciência e a Tecnologia

Identifier for the funding entity: http://dx.doi.org/10.13039/501100001871

Type of identifier of the funding entity: Crossref Funder

Number for the project, award or grant: UIDB/04111/2020