First Workshop on Scientific-Driven Deep Learning (SciDL)

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
Invited Speakers
  • Frank Noe (FU Berlin) paper video
    • Title: PauliNet: Deep neural network solution of the electronic Schrödinger Equation
  • Yasaman Bahri (Google Brain) video
    • Title: Learning Dynamics of Wide, Deep Neural Networks: Beyond the Limit of Infinite Width
  • 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
N. Benjamin Erichson
N. Benjamin Erichson
Assistant Professor