Author:
Makahleh Firas,Nassar Anas
Abstract
Engineering and science-related problems become more complicated as human knowledge evolves. This complication includes apparatus geometry and operational environment such as extreme variations in pressure and temperature. Analytical solution for such problems needs many assumptions that underestimate the problem under study and could lead to unrealistic results. Moreover, an experimental setup for a certain problem is constrained by the prototype size and each experiment is set up for certain operating conditions. This leads to building up many setups to deal with changes in size and operating conditions and, therefore, the prototype validation becomes very expensive and time-consuming. This calls for modeling and simulation approaches to deal with such engineering problems with the powerful computational capabilities available nowadays. Real-world patterns and processes are roughly modeled by scientific models. They may be refuted because they are representations, which are by definition imperfect. Models, however, are quite helpful for a variety of reasons. They first give us a method to comprehend procedures that would otherwise be incomprehensible to us. They also give scientists a base on which to build new research and theories. Finally, modeling and simulation reduce the time and cost of prototyping.
Reference40 articles.
1. Nassar A, Rai NK, Sen O, Udaykumar HS. Modeling mesoscale energy localization in shocked HMX, part I: Machine-learned surrogate models for the effects of loading and void sizes. Shock Waves. 2019;:537-558
2. Nassar A. Physics-Based Machine-Learned Models for Multi-Scale Materials Response to Shock Loads [thesis]. Iowa: The University of Iowa; 2019
3. Sivamayil K, Rajasekar E, Aljafari B, Nikolovski S, Vairavasundaram S, Vairavasundaram I. A systematic study on reinforcement learning based applications. Energies. 2023;(3):1512
4. Benti NE, Chaka MD, Semie AG. Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects. Sustainability. 2023;(9):7087
5. Razzaghi P, Tabrizian A, Guo W, Chen S, Taye A, Thompson E, et al. A survey on reinforcement learning in aviation applications. arXiv. 2022:1-14