1717 Recent Advances in Scientific Machine Learning and Uncertainty Quantification Methods for Modeling Complex Systems

  • Ramin Bostanabad, University of California, Irvine
  • Miguel Bessa, Delft University of Technology

The recent technological advancements have drastically accelerated the analysis, design, and deployment of highly complex systems such as super-compressible materials, autonomous vehicles, novel microelectronics, bio-inspired underwater vehicles, and origami-inspired sensors with tunable sensitivities. The objective of this mini symposium is to highlight the recent advancements in deep learning, computational statistics, high-performance computing, and mathematics to bring contributions to emerging applications in solid mechanics, materials science, manufacturing, fluid dynamics, multi-scale and/or multi-physics modeling, uncertainty quantification, inverse problems, and other relevant topics.

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