Scientific Machine Learning Webinar Series

This webinar series and panel events are organized by Keith Phuthi, Varun Shankar and Venkat Viswanathan with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.

Webinar 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

How To Join

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

Suggest speakers:


Upcoming/Ongoing Events

Spring 2023:

Past Webinars

Fall 2022:

Generative Models:

Differentiable Physics:

Symmetries, Physical Systems and Machine Learning:

Machine Learning Potentials and Force Fields for Materials Chemistry:

Machine Learning meets Information Theory and Statistical Mechanics:

Machine Learning in Fluid Dynamics:

VC Panel on Quantum Computing (May 11th, Tuesday at 2 pm Pacific Time):

Quantum Machine Learning:

The organizers would like to thank Jarrod McLean (Google) and Zlatko K. Minev (IBM) for suggestions of speakers and session chairs.

Deployment of ML in the Industry:

Molecular ML for Drug Discovery:

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 Panel (March 23rd):

Physics-Regularized ML:

ML Obeying Physical Symmetries:

ML-Embedded Physical Models:


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 mphuthi[at], varunshankar[at] and venkvis[at]

Video Recordings

A Google Drive link to the Video recordings are available on request on the slack or by emailing the organizers.

Subscribe to the SciML Newsletter

* indicates required