Computational Science Initiative Event

"Physics Informed Machine Learning"

Presented by Michael (Mischa) Chertkov, Los Alamos National Lab

Tuesday, February 13, 2018, 10:30 am — Seminar Room, Bldg. 725

Machine Learning (ML) capabilities are in a phase of tremendous growth, and there is great opportunity to point these tools toward physical modeling. The challenge is to incorporate domain expertise from traditional scientific discovery into next-generation ML models. We propose to develop new Physics Informed Machine Learning (PIML) algorithms that extend cutting-edge computational and algorithmic ML tools and merge them with physical knowledge in the form of constraints, symmetries, and domain expertise regarding effective degrees of freedom. This PIML methodology is illustrated on the following four enabling examples:

1. Topology and Parameter Estimation in Power Grids [based on arXiv:1710.10727]
2. Creating Turbulent Flows with Deep Learning [based on an APS/DFD2017 abstract]
3. Learning Graphical Models [Science 2018 in print; arXiv:1612.05024]
4. Renormalization of Tensor Networks (Graphical Models) [based on arXiv:1801.01649 and ICML2018 submission]

Hosted by: Frank Alexander

12859  |  INT/EXT  |  Events Calendar


Not all computers/devices will add this event to your calendar automatically.

A calendar event file named "calendar.ics" will be placed in your downloads location. Depending on how your device/computer is configured, you may have to locate this file and double click on it to add the event to your calendar.

Event dates, times, and locations are subject to change. Event details will not be updated automatically once you add this event to your own calendar. Check the Lab's Events Calendar to ensure that you have the latest event information.