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Micromechanics-based deep learning for composites

CHAIR networking seminar, organized by the AI for Scientific Data Analysis theme.


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Assistant Prof. Mohsen Mirkhalaf


During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites.

One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using conventional optimization approaches typically results in cumbersome procedures due to huge dimensions of the design space and high computational expense of full-field simulations.

In recent years, deep learning techniques have been found to be promising methods to increase the efficiency and robustness of a variety of algorithms in multi-scale modeling and design of composites.

In this presentation, I will talk about our recent activities in usage of artificial neural networks and micromechanical simulations to develop significantly fast and highly accurate models for composite materials.


AI for Scientific Data Analysis

This theme is about utilizing the power of AI as a tool for scientific research. AI can be applied to, and potentially speed up, discovery and utilization in a variety of research disciplines, such as microscopy, physics, biology, chemistry, and astronomy.