Integrated Multiscale Modeling-Simulation (MMS) and Machine Learning (ML)-Based Design and Development of Novel Technologies, Systems, and Processes
<p id="p1">The development of novel technologies, systems, and processes is conventionally complemented by experimental testing. However, experimental tools for testing and examining the results are expensive, and their use is time-consuming. In this context, to accelerate the development, commercia...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2023
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1139261 |
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| Sumario: | <p id="p1">The development of novel technologies, systems, and processes is conventionally complemented by experimental testing. However, experimental tools for testing and examining the results are expensive, and their use is time-consuming. In this context, to accelerate the development, commercialization, utilization, and problem solutions of novel technologies, systems, and processes, the simultaneous use of computational and experimental tools such as hierarchical integrated machine learning (ML)-assisted multi-scale modeling-simulation (MMS) and experimental approaches is essential. These approaches greatly improve the entire technology development process by reducing cost and time and allow us to tackle problems that cannot be solved using theoretical or experimental methods alone. In this chapter, we describe ways in which integrated multiscale modeling-simulation and machine learning have been leveraged to facilitate the design and development of novel technologies, systems, and processes. We first provide a taxonomy of multiscale modeling-simulation and machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of opportunities and existing research using multiscale modeling-simulation and machine learning for the design and development of novel technologies, systems, and processes. Finally, we propose future research directions and discuss important considerations for deployment.</p> |
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| ISBN: | 978-1-83769-706-9 |
| DOI: | 10.5772/intechopen.1002381 |