1st Workshop on Scientific-Driven Deep Learning (SciDL)

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8:00-14:30 (PST) on Wednesday July 1, 2020

Location: Zoom

Register here: https://forms.gle/o1WCSBTbeoahj5ww8

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Deep learning is playing a growing role in the area of fluid dynamics, climate science and in many other scientific disciplines. Classically, deep learning has focused on an model agnostic learning approaches ignoring any prior knowledge that is known about the problem under consideration. However, limited data can severely challenge our ability to train complex and deep models for scientific applications. This workshop focuses on scientific-driven deep learning to explore challenges and solutions for more robust and interpretable learning.

Key Note Speakers

• George Em Karniadakis (Brown University) paper video
  • Title: DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators

• Michael P. Brenner (Harvard University) video
  • Title: Machine Learning for Partial Differential Equations

Invited Speakers

• Frank Noe (FU Berlin) paper video
  • Title: PauliNet: Deep neural network solution of the electronic Schrödinger Equation

• Alejandro Queiruga (Google, LLC) video
  • Title: Continuous-in-Depth Neural Networks

• Michael Muehlebach (UC Berkeley) paper video
  • Title: Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives

• Yasaman Bahri (Google Brain) video
  • Title: Learning Dynamics of Wide, Deep Neural Networks: Beyond the Limit of Infinite Width

• Elizabeth Qian (MIT) paper video
  • Title: Lift & Learn: Analyzable, Generalizable Data-Driven Models for Nonlinear PDEs

• Lars Ruthotto (Emory University) video
  • Title: Deep Neural Networks Motivated by PDEs

• Tess Smidt (LBL) video
  • Title: Neural Networks with Euclidean Symmetry for Physical Sciences

• Omri Azencot (UCLA) paper video
  • Title: Robust Prediction of High-Dimensional Dynamical Systems using Koopman Deep Networks

Organizers

• N. Benjamin Erichson (UC Berkeley)
• Michael W. Mahoney (UC Berkeley)
• Tess Smidt (LBL)
• Steven L. Brunton (University of Washington)
• J. Nathan Kutz (University of Washington)