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
Upcoming Webinars
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 22: Bharath Ramsundar, Creator of DeepChem & Stanford CS PhD Physical Theories and Differentiable Programs
Session Chair: Prof. Venkat Viswanathan, Carnegie Mellon University
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)
Dec 3: Alok Warey, General Motors & University of Texas at Austin PhD Deep Learning for Vehicle Systems
Session Chair: Aniruddha Mukhopadhyay, ANSYS, Inc.
Dec 10: Jesse Bettencourt
, University of Toronto PhD Neural Ordinary Differential Equations
Session Chair: Prof. Zico Kolter, Carnegie Mellon University
The seminar series is supported by the ARPA-E DIFFERENTIATE program and the Carnegie Mellon Presidential Fellowship.
Conception
A panel discussion on the topic Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization crystallized important considerations while applying machine learning methods to limited-data engineering applications. Often it's useful to synergistally stack the ML models to the extent possible with the known physics of the problem for effective learning even in low-data regimes.
Questions?
Email the organizers at dkrishn1[at]andrew.cmu.edu and venkvis[at]cmu.edu
Video Recordings
Video recordings are available (login details available on request) here