The mini-course organized by Dilip Krishnamurthy and Venkat Viswanathan is a seminar series with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.
Seminar Format: Presenters can use the opportunity to showcase a paper or two with an explicit focus on the methodology and approach. Duration: 40 minutes of methodology + 20 minutes of implementation (code) walk-through + 20 minutes of questions. Invited session chairs will guide the discussion along with offering their perspective on the field. The Q&A session is typically very interactive with a small group of enthusiastic audience.
Seminar Time: Thursdays 11 am to 12:30 pm Eastern Time
Jan 14: Miles Cranmer, Princeton Astrophysics PhD
Time Symmetries and Neurosymbolic Learning for Dynamical Systems
Session Chair: Prof. Phiala Shanahan, Massachusetts Institute of Technology
Jan 28: Gurtej Kanwar, Massachusetts Institute of Technology PhD Gauge-Invariant Machine-Learned Flow-Based Sampling Algorithms
Panel Discussion on Open Challenges in ML:
This session is focused on discussing challenges and technological bottlenecks at the intersection of machine learning and science/engineering. Industry leaders at original equipment manufacturers (OEMs) and venture capitalists (VCs) will provide their perspective and directions for research and development. We anticipate that this session will facilitate effective TT & O (Tech. Transfer and Outreach).
Oct 29 (note schedule change): Jan Hermann
, Freie Universität Berlin & Humboldt University of Berlin Physics PhD Deep neural network solution of the electronic Schrödinger equation
Session Chair: Prof. Giuseppe Carleo, École polytechnique fédérale de Lausanne (EPFL)