<div class="csl-bib-body">
<div class="csl-entry">Wenger, U., Ipp, A., Müller, D., & Holland, K. (2023, June 28). <i>Machine learning a fixed point action</i> [Conference Presentation]. Machine learning for lattice field theory and beyond 2023, Trento, Italy. http://hdl.handle.net/20.500.12708/193366</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/193366
-
dc.description
Lattice gauge-equivariant convolutional neural networks (LGE-CNNs) can be used to form arbitrarily shaped Wilson loops and can approximate any gauge-covariant or gauge-invariant function on the lattice. Here we use LGE-CNNs to describe fixed point (FP) actions which are based on inverse renormalization group transformations. FP actions are classically perfect, i.e., they have no lattice artefacts on classical gauge-field configurations satisfying the equations of motion, and therefore possess scale invariant instanton solutions. FP actions are tree–level Symanzik–improved to all orders in the lattice spacing and can produce physical predictions with very small lattice artefacts even on coarse lattices. They may therefore provide a solution to circumvent critical slowing down towards the continuum limit.
-
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
-
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
-
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
-
dc.language.iso
en
-
dc.subject
Convolutional Neural Networks
en
dc.subject
Lattice Gauge Theory
en
dc.title
Machine learning a fixed point action
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.contributor.affiliation
University of the Pacific, United States of America (the)
-
dc.relation.grantno
P 32446-N27
-
dc.relation.grantno
P 34764-N
-
dc.relation.grantno
P 34455-N
-
dc.type.category
Conference Presentation
-
tuw.project.title
Glasma-Simulationen mit maschinellem Lernen hochskalieren
-
tuw.project.title
Simulation der frühesten Stadien von Schwerionenkollisionen
