Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models [PDF]
Physical Review Research, 2023 Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics. We develop a general approach to constructing gauge-invariant or anyonic-symmetric autoregressive neural networks, including a wide range of ...
Di Luo +5 more
doaj +2 more sources
High-dimensional and permutation invariant anomaly detection [PDF]
SciPost Physics, 2023 Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as
Vinicius Mikuni, Benjamin Nachman
doaj +2 more sources
Invariant representation of physical stability in the human brain [PDF]
eLife, 2021 Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and hence likely to stay the same, or unstable, and hence likely to change in the immediate future.
RT Pramod +3 more
openaire +4 more sources
Absolute Geometry: From Basics to the π-rule of the Ф-invariant Physics
Advances in Theoretical & Computational Physics, 2021 This is to clarify in more detail some basic aspects of absolute geometry and discuss what is the π-rule in physics unified by the universal Ф ...
openalex +2 more sources
Lines of invariant physics in the isotropic phase of the discotic Gay-Berne model
Journal of Non-Crystalline Solids: X, 2022 Saeed Mehri +3 more
openalex +2 more sources
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics [PDF]
arXiv.org, 2022 Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools.
Alexander Bogatskiy +3 more
openalex +3 more sources
Configurational temperature in active matter. I. Lines of invariant physics in the phase diagram of the Ornstein-Uhlenbeck model. [PDF]
Physical Review E, 2022 This paper shows that the configurational temperature of liquid-state theory, T_{conf}, defines an energy scale, which can be used for adjusting model parameters of active Ornstein-Uhlenbeck particle (AOUP) models in order to achieve approximately ...
Shibu Saw, L. Costigliola, J. Dyre
semanticscholar +1 more source
Training neural operators to preserve invariant measures of chaotic attractors [PDF]
Neural Information Processing Systems, 2023 Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate.
Ruoxi Jiang +3 more
semanticscholar +1 more source
Scale-invariant resonance tagging in multijet events and new physics in Higgs pair production [PDF]
, 2013 A bstractWe study resonant pair production of heavy particles in fully hadronic final states by means of jet substructure techniques. We propose a new resonance tagging strategy that smoothly interpolates between the highly boosted and fully resolved ...
M. Gouzevitch +5 more
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Radial bound states in the continuum for polarization-invariant nanophotonics [PDF]
Nature Communications, 2022 All-dielectric nanophotonics underpinned by the physics of bound states in the continuum (BICs) have demonstrated breakthrough applications in nanoscale light manipulation, frequency conversion and optical sensing.
Lucca Kühner +7 more
semanticscholar +1 more source