Submission Deadline: 31 October 2024 View: 314 Submit to Special Issue
The implementation of artificial intelligence has permeated nearly all areas of academia and industry. The field of computational mechanics has greatly benefited from the application of machine learning, especially in cases where traditional computational methods are inefficient and inapplicable, due to its versatility and power of this framework. In this special issue, we will aim to cover the following topics at the intersection of machine learning and mechanics:
(1) Advanced data-driven simulation techniques for complex problems in solid and fluid mechanics, including the integration of machine learning algorithms and high-performance computing for faster and more accurate simulations.
(2) Artificial intelligence (AI) aided mechanical design, including the use of generative design algorithms, reinforcement learning, and other AI techniques to optimize designs.
(3) Development and application of machine learning-based inverse and forward methods for solving inverse problems in mechanics, such as material parameter identification and damage detection, and for predicting mechanical behavior from input parameters.
(4) Machine learning-based multiscale modeling approaches, including the development of new methods for bridging the gap between micro and macro scales in materials science and mechanics, and for efficiently simulating complex multiphysics problems.
(5) Other related topics, such as the use of machine learning in other areas of mechanics and engineering.
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