1701 Applications of Artificial Intelligence and Machine-Learning Methods to Mechanics, Materials, Medicine, and Engineering

  • Shaofan Li, University Of California, Berkeley
  • Vickie Shim, The University of Auckland
  • Ying Li, The University of Connecticut
  • Harold Park, Boston University
  • Shingo Urata, AGC Inc.

The rapid development of computational technologies in artificial intelligence (AI) and machine learning (ML) has started to revolutionize many aspects of our lives, while also significantly changing the way computational modeling and simulation are performed. Indeed, ML and other intelligent statistics techniques extend the applicability of computational mechanics, molecular modeling, topology optimization, and medical design, for instance, by combining theory-based simulations and data-based inference. In this mini-symposium, we aim to provide a forum for the latest developments in applying AI-based technologies, such as ML in applied mechanics, materials, medicine, and engineering problems in general. We welcome all contributions, with interests in these areas:

1. Applications of computational data science to solve engineering problems.
2. Computational probabilistic mechanics.
3. ML approaches to molecular dynamics and finite element methods.
4. AI-based methods and approaches to additive manufacturing and 3D printing technologies.
5. Data-driven methods for design, synthesis, and characterization of polymers and their composites.
6. ML approaches in medicine and diagnostic tools.
7. AI-based approaches to materials characterization and analysis.
8. Digital twin technology with computational modeling, and
9. Model order reduction, reduced order modeling, and their applications to solve engineering problems.

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