Time table of the posters sessions - News!!

Posters Session 1, Monday 13th 

P 1.1 - E. Caschera et al., « Data base driven characterization of zonal flows in global, flux driven, gyrokinetic simulations »

P 1.2 - S.Y. Chung et al., « Numerical optimization and compression of chemical reaction rate coefficient set for fluorocarbon plasma simulation »

P 1.3 - K. Hasamada et al., « Spectrum analysis for detection of turbulent transport bifurcation in a linear magnetized plasma »

P 1.4 - M. Honda et al. « Fast computation of the steady-state transport solver GOTRESS assisted by a deep neural network modeling »

P 1.5 - K. Ikuse et al., « Sputtering Yield Prediction by Machine Learning »

P 1.6 - T. Kurosawa et al., « Bayesian Electron Density Inference in LHD with High Temporal and Spatial Resolutions From Two Complementary Diagnostics »

P 1.7 - M. Sasaki et al, « Extraction of spatial structures of intermittent events using dynamical mode decomposition »

P 1.8 - P. Ghendrih et al., « Verification and accuracy using output data of code production runs "

P 1.9 - A. Sasaki et al., « A Markov-chain Monte-Carlo approach for collisional radiative model of complex multiple-charged ions»

P 1.10 - E. Serre et al., « Towards the assimilation of experimental data for tokamak edge plasma transport simulations »

P 1.11 - T. Yokoyama et al., « Feature extraction using exhaustive search in disruption prediction based on JT-60U experimental data »

P 1.12 - A. Pau et al., « Identification and comparison of operational boundaries using data-driven models on JET and D3D »

P 1.13 - S. De Pacuale et al., "Applications of Dynamic Mode Decomposition on Simulation Data"

Posters Session 2, Thursday 16th 

P 2.1 - K. Fujii et al., « Statistical Completion and Validation of Atomic Energy Level Database Based on Low Rank Nature of Isoelectronic Sequence »

P 2.2 - I. Murakami et al., « NIFS Atomic and Molecular Numerical Database for Collision Processes in Plasmas »

P 2.3 - R. Guinee et al., « A Novel Kalman Filter Bank Methodology For Time Series Prediction In Forecasting Applications »

P 2.4 - S. Hamaguchi, « Collaborative Research in Data-Driven Plasma Science, JSPS Core-to-Core Program »

P 2.5 - K. Kamataki et al., « Predictive insight based on grey box analysis of plasma process data »

P 2.6 - K. Montes et al., « Exploration of Common Physics-Based Indicators of Disruption Precursors across Multiple Tokamaks» 

P 2.7 - E. Narita et al., « Neural network modeling towards fast kinetic profile prediction and understanding profile formation mechanisms in tokamak plasmas »

P 2.8 - S. Ohdachi et al., « Visualization of the magnetic island using two tangentially viewing camera systems on toroidally confined fusion devices »

P 2.9 - A. Piccione et al., « Determination of the ideal no-wall stability limit in NSTX through decision boundaries »

P 2.10 - R. Sabot et al., « A decade of fluctuation reflectometry measurements on Tore Supra: trends emerging from a systematic analysis of the database »

P 2.11 - T. Yoshitake et al., « Informatics Approach Considering Three Dimensional Cell Structure to Optimize Plasma Irradiation Time in Plasma Gene Transfer Method »

P 2.12 - K. Yuichi et al., « Instantaneous frequency analysis for turbulent fluctuation in a linear magnetized plasma »

P 2.13 - M. Jinno et al., " Informatics optimization of the parameters for individual cell kind in micro-plasma gene transfection"