Scientific Machine Learning Mini-Course
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 21: Shaojie Bai, Carnegie Mellon University PhD
Deep Equilibrium Models
Session Chair: Stephan Hoyer, Google Research - 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).VC perspective:
- Joel Moxley, Breakthrough Energy
- Lisha Li, Rosebud AI
- James Hardiman, DCVC (Data Collective)
Industry perspective:
- Shailendra Kaushik, General Motors
Past Webinars
Physics-Regularized ML:
- Oct 1: Dilip Krishnamurthy, Carnegie Mellon University PhD
Physics-Informed Neural Networks
Session Chair: Bharath Ramsundar, Stanford PhD - Oct 8: Xiaowei Jia, Asst. Prof. @ University of Pittsburgh & University of Minnesota PhD
Physics-Guided Machine Learning for Scientific Discovery
Session Chair: Jason Koeller, Citrine Informatics
ML Obeying Physical Symmetries:
- Oct 15 (note schedule change): Bingqing Cheng, Trinity College Cambridge & EPFL PhD
Machine-Learning Potentials: What Works and What Doesn’t
Session Chair: Prof. Frank Noe, Freie Universität Berlin - 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) - Nov 5 (note schedule change): Tess Smidt and Mario Geiger, Lawrence Berkeley Laboratory & École polytechnique fédérale de Lausanne, respectively
Neural Networks With Euclidean Symmetry for Physical Sciences and e3nn: A Modular PyTorch Framework for Euclidean Neural Networks
Session Chair: Prof. Risi Kondor, University of Chicago
ML-Embedded Physical Models:
- Nov 12: Li Li, Google Accelerated Science & UC Irvine PhD
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Session Chair: Prof. Vikram Gavini, University of Michigan - Nov 19: Christopher Rackauckas and Alec Bills, Massachusetts Institute of Technology & Pumas-AI, and Carnegie Mellon University, respectively
Universal Ordinary Differential Equations and Its Application to an Engineering Challenge
Session Chair: Prof. Srinivas (Sai) Ravela, Massachusetts Institute of Technology - 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
How To Join
Add Webinar Calendar
Zoom Link
Webinar ID: 992 4479 8052
Passcode: 919401
Resources
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