Scientific Machine Learning Mini-Course

The mini-course organized by Dilip Krishnamurthy and Venkat Viswanathan is a short seminar series (12+ weeks starting Oct 1, 2020) 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.

Seminar Time: Thursdays 8:30 pm to 10 pm Eastern Time

Speakers and Topics

Physics-Regularized ML:

ML Obeying Physical Symmetries:

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:
Industry perspective:

ML-Embedded Physical Models:

Overleaf Template for Methodology Walk-Through

Colab Notebook Format for Code Walk-Through

How To Join

Add Webinar Calendar
Zoom Link
Webinar ID: 992 4479 8052
Passcode: 919401


The seminar series is supported by the ARPA-E DIFFERENTIATE program and the Carnegie Mellon Presidential Fellowship.


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.


Email the organizers at dkrishn1[at] and venkvis[at]

Video Recordings

Video recordings are available (login details available on request) here

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