arXiv subjects

Jun-Yin Huang

Publications and source records attributed to Jun-Yin Huang.

4 recordsLinked to original sources

arXiv2025

Optimizing disorder with machine learning to harness synchronization

Disorder is often considered detrimental to coherence. However, under specific conditions, it can enhance synchronization. We develop a machine-learning framework to design optimal disorder configurations that maximize phase synchronization. In particular, utilizing the system of coupled nonlinear pendulums with disorder and noise, we train a feedforward neural network (FNN), with the disorder parameters as input, to predict the Shannon entropy index that quantifies the phase synchronization strength. The trained FNN model is then deployed to search for the optimal disorder configurations in the high-dimensional space of the disorder parameters, providing a computationally efficient replacement of the stochastic differential equation solvers. Our results demonstrate that the FNN is capable of accurately predicting synchronization and facilitates an efficient inverse design solution to optimizing and enhancing synchronization.

nlin.AO↗

arXiv2025

Floquet quantum many-body scars in the tilted Fermi-Hubbard chain

The one-dimensional tilted, periodically driven Fermi-Hubbard chain is a paradigm in the study of quantum many-body physics, particularly for solid-state systems. We uncover the emergence of Floquet scarring states, a class of quantum many-body scarring (QMBS) states that defy random thermalization. The underlying physical mechanism is identified to be the Floquet resonances between these degenerate Fock bases that can be connected by one hopping process. It is the first-order hopping perturbation effect. Utilizing the degenerate Floquet perturbation theory, we derive the exact conditions under which the exotic QMBS states emerge. Phenomena such as quantum revivals and subharmonic responses are also studied. Those results open the possibility of modulating and engineering solid-state quantum many-body systems to achieve nonergodicity.

quant-ph↗

arXiv2024

Reconstructing dynamics from sparse observations with no training on target system

In applications, an anticipated situation is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed from the limited observations without any training data? This problem defies any known traditional methods of nonlinear time-series analysis as well as existing machine-learning methods that typically require extensive data from the target system for training. We address this challenge by developing a hybrid transformer and reservoir-computing machine-learning scheme. The key idea is that, for a complex and nonlinear target system, the training of the transformer can be conducted not using any data from the target system, but with essentially unlimited synthetic data from known chaotic systems. The trained transformer is then tested with the sparse data from the target system. The output of the transformer is further fed into a reservoir computer for predicting the long-term dynamics or the attractor of the target system. The power of the proposed hybrid machine-learning framework is demonstrated using a large number of prototypical nonlinear dynamical systems, with high reconstruction accuracy even when the available data is only 20% of that required to faithfully represent the dynamical behavior of the underlying system. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the extreme situation where training data does not exist and the observations are random and sparse.

cs.LG↗

arXiv2023

Symmetry dictated universal helicity redistribution of Dirac fermions in transport

Helicity is a fundamental property of Dirac fermions. Yet, the general rule of how it changes in transport is still lacking. We uncover, theoretically, the universal spinor state transformation and consequently helicity redistribution rule in two cases of transport through potentials of electrostatic and mass types, respectively. The former is dictated by Lorentz boost and its complex counterpart in Klein tunneling regime, which establishes miraculously a unified yet latent connection between helicity, Klein tunneling, and Lorentz boost. The latter is governed by an abstract rotation group we construct, which reduces to SO(2) when acting on the plane of effective mass and momentum. They generate invariant submanifolds, i.e., leaves, that foliate the Hilbert space of Dirac spinors. Our results provide a basis for unified understanding of helicity transport, and may open a new window for exotic helicity-based physics and applications in mesoscopic systems.

cond-mat.mes-hall↗