4:00 pm
GCIS Room W301
"Equivalent Neural Networks for Modeling Physical Interactions: Curve Fitting or a New Way of Understanding Nature"
In the last few years there has been an explosion of interest in using machine learning for modeling physical and chemical systems. Research in this field ranges from using ML tools narrowly, such as to just learn the force fields that are plugged into a molecular dynamics simulation system, to trying to use AI as a drop-in replacement for PDE solvers or entire protein structure prediction pipelines. Many researchers feel that the most productive way to harness AI in science will be to tightly couple physical modeling with the more statistical, data driven pilosophy of ML. One small step along this way has been the development of equivariant neural networks, which are able to explicitly account for some physical symmetries and conservation laws. In this talk I will give a broad, somewhat mathematical, introduction to this subject and illustrate it with some examples. Host: Sure Vaikuntanathan, svaikunt@uchicago.edu. For persons who may need assistance contact Brenda Thomas at bthomas@uchicago.edu