Dr. Paris Perdikaris, University of Pennsylvania
Dr. Maziar Raissi, Brown University
Advances in machine learning are continuously penetrating computational science and engineering. In this course we plan to review recent advances in deep learning with a particular focus on the development of data-driven algorithms for model discovery, forecasting, and uncertainty quantification in physical and engineering systems.
In this course we will (i) present a comprehensive review of state-of-the-art deep learning tools including feed-forward/convolutional/recurrent neural networks, variational auto-encoders, and generative adversarial networks, (ii) show how these data-driven models can be constrained to encode physical priors and domain knowledge, (iii) demonstrate how they can help us distill "hidden physics" from raw data and construct scalable and predictive surrogate models, (iv) provide a collection of diverse applications in computational science, including both forward and inverse problems in the presence of uncertainty.
Our goals for this course are threefold: (i) cover fundamental methodological and algorithmic concepts, (ii) showcase a collection of practical applications, and (iii) design a series of hands-on tutorials that will illustrate key practical and implementation aspects. Attendees will leave this course with a well-rounded understanding of the capabilities brought by deep learning in a wide range of applications in computational science and engineering. They will also sharpen their hands-on skills and familiarize themselves with how to adapt these tools to their respective application domains.