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Workshop on Scientific-Driven Deep Learning (SciDL)

machine learning dynamical systems applied physics

Audience:	Researchers in the topic
Conference date:	01-Jul-2020
Organizers:	N. Benjamin Erichson*, Michael Mahoney, Steven Brunton, Nathan Kutz
	*contact for this listing

When: 8:00-14:30pm (PST) on Wednesday July 1, 2020 Where: berkeley.zoom.us/j/95609096856 Details: scidl.netlify.app/

Upcoming talks

Past talks

Your time			Speaker	Title
Wed	Jul 01	21:05	Omri Azencot	Robust Prediction of High-Dimensional Dynamical Systems using Koopman Deep Networks
Wed	Jul 01	20:40	Tess Smidt	Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomic Geometries
Wed	Jul 01	20:15	Lars Ruthotto	Deep Neural Networks Motivated by PDEs
Wed	Jul 01	19:50	Elizabeth Qian	Lift & Learn: Analyzable, Generalizable Data-Driven Models for Nonlinear PDEs
Wed	Jul 01	19:00	Michael P. Brenner	Machine Learning for Partial Differential Equations
Wed	Jul 01	17:15	Yasaman Bahri	Learning Dynamics of Wide, Deep Neural Networks: Beyond the Limit of Infinite Width
Wed	Jul 01	16:50	Michael Muehlebach	Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
Wed	Jul 01	16:25	Alejandro Queiruga	Continuous-in-Depth Neural Networks through Interpretation of Learned Dynamics
Wed	Jul 01	16:00	Frank Noe	PauliNet: Deep neural network solution of the electronic Schrödinger Equation
Wed	Jul 01	15:10	George Em Karniadakis	DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators
Wed	Jul 01	15:00	Nathan Kutz	Opening remarks

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