Machine learning for physics: gauge-equivariant architectures

Phiala Shanahan (MIT)

23-Mar-2021, 17:00-18:00 (3 years ago)

Abstract: As machine learning algorithms continue to enable and accelerate physics calculations in novel ways, the development of tailored physics-informed machine learning approaches is becoming more sophisticated, impactful, and important. I will give some broad context for this developing area, with a focus on the challenge of exact sampling from known probability distributions as relevant to lattice quantum field theory calculations in particle and nuclear physics. I will discuss in particular flow-based generative models, and describe how guarantees of exactness and the incorporation of complex symmetries (e.g., gauge symmetry) into model architectures can be achieved. I will show the results of proof-of-principle studies that demonstrate that sampling from generative models can be orders of magnitude more efficient than traditional Hamiltonian/hybrid Monte Carlo approaches in this context.

HEP - phenomenologyHEP - theorymathematical physics

Audience: researchers in the topic


NHETC Seminar

Series comments: Description: Weekly research seminar of the NHETC at Rutgers University

Livestream link is available on the webpage.

Organizers: Christina Pettola*, Sung Hak Lim, Vivek Saxena*, Erica DiPaola*
*contact for this listing

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