Tuesday, February 19, 2019 – Thursday, February 21, 2019 [www] Santa Fe
Recent breakthroughs in machine learning (ML), including the stunning successes of AlphaZero and AlphaGo, have demonstrated tremendous potential for transforming scientific computing. The application of these exciting advances in algorithms and computer architectures to the computational modeling and simulation community introduces several additional requirements and challenges beyond traditional applications such as data analytics and computer vision. These include physical constraints (the subject of CNLS Physics-Informed Machine Learning workshops in 2016 and 2018), the need for uncertainty quantification (UQ), and computational requirements for embedded ML models, e.g. for parameter tuning, sub-scale physics models, optimization, UQ, or data assimilation. This workshop will bring together international leaders in the development and application of ML methods for fluid and solid dynamics.