Scientific Machine Learning Webinar Series

The SciML webinar has moved to the University of Michigan's MICDE. Find the website, calendar, Zoom link and talks at . If you were on our mailing list, look out for an email to consent being added to the new SciML webinar mailing list.

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

Suggest speakers:


Past Webinars

Spring 2023:

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 mkphuthi[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.