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Jihoon Seo

Publications and source records attributed to Jihoon Seo.

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Universality class of a spinor Bose-Einstein condensate far from equilibrium

Scale invariance and self-similarity in physics provide a unified framework to classify phases of matter and dynamical properties near equilibrium in both classical and quantum systems. This paradigm has been further extended to isolated many-body quantum systems driven far from equilibrium, where physical observables exhibit dynamical scaling with universal scaling exponents. Universal dynamics appear in a wide range of scenarios, including cosmology, quark-gluon matter, ultracold atoms, and quantum spin magnets. However, how universal dynamics depend on the symmetry of the underlying Hamiltonian in nonequilibrium systems remain an outstanding challenge. Here, we report on the classification of universal coarsening dynamics in a quenched two-dimensional ferromagnetic spinor Bose gas. We observe spatiotemporal scaling of spin correlation functions with distinguishable scaling exponents that characterize binary and diffusive fluids. The universality class of the coarsening dynamics is determined by the symmetry of the order parameter and the dynamics of the topological defects, such as domain walls and vortices. Our results provide a categorization of the universality classes of far from equilibrium quantum dynamics based on symmetry properties of the system.

cond-mat.quant-gas

Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM

Polluting fine dusts in South Korea which are mainly consisted of biomass burning and fugitive dust blown from dust belt is significant problem these days. Predicting concentrations of fine dust particles in Seoul is challenging because they are product of complicate chemical reactions among gaseous pollutants and also influenced by dynamical interactions between pollutants and multiple climate variables. Elaborating state-of-art time series analysis techniques using deep learning, non-linear interactions between multiple variables can be captured and used to predict future dust concentration. In this work, we propose the LSTM based model to predict hourly concentration of fine dust at target location in Seoul based on previous concentration of pollutants, dust concentrations and climate variables in surrounding area. Our results show that proposed model successfully predicts future dust concentrations at 25 target districts(Gu) in Seoul.

cs.LG