Learning How to Break Symmetry With Symmetry-Preserving Neural Networks - IPAM at UCLA
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Intro
Overview
Why is symmetry useful
Invariant vs equivariant models
Equivariant methods
Global symmetry equivalent
Questions
Applications
Molecular force fields
Scaling
Longrange interactions
Predicting charge density
Using a neural network
First paper
Competition
Efficiency
Advanced properties
Neural networks
Representation theory
Reducible representation
Future spaces
Spherical harmonic projections
Invariance
General questions
Emergent behavior
Curious principle
Structural phase transitions
First case
Order parameters
Why not just train a model
Another example
Taught by
Institute for Pure & Applied Mathematics (IPAM)