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How unconstrained machine-learning models learn physical symmetries
<p>The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development... -
Representing spherical tensors with scalar-based machine-learning models
Rotational symmetry plays a central role in physics, providing an elegant framework to describe how the properties of 3D objects – from atoms to the macroscopic scale –... -
Probing the effects of broken symmetries in machine learning
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to...
