• Shaoping Xiao, University of Iowa

Riding with the current wave of Artificial Intelligence (AI), many engineers and scientists have adopted machine learning and deep learning as powerful tools in various engineering disciplines, including biomechanics, materials science, control, robotics, and more. Deep learning, utilizing artificial neural networks, is the most effective, supervised, time, and cost-efficient machine learning approach. It has been successfully used in image classification, face recognition, language translation, etc. This symposium aims to bring researchers together to share the insights of AI in current research projects and promote the applications of deep learning in computational materials science and engineering disciplines. The topics of interest to this symposium include, but are not limited to, the following:
1. Introduction to the state-of-the-art deep learning methods: artificial neural network, convolutional neural network, recurrent neural network, etc.
2. Deep learning in material design optimization
3. Artificial neural networks in multiscale modeling and simulations
4. Image processing and detection in materails science research
5. Data-driven modeling and simulation in materials science and mechanics
6. Other machine learning and AI approaches in computational materials science and engineering