Symposium 1 : Interdisciplinary Simulation and Machine Learning

OrganizerSeiki Saito (Yamagata Univ.)
Co-organizerTakahiro Kenmotsu (Doshisha Univ.), Kazuo Hoshino (Inst. Keio Univ.), Satoshi Togo (Inst. Univ. of Tsukuba)
ObjectiveThis interdisciplinary symposium aims to unite experts and researchers across diverse scientific domains, emphasizing the integration of simulations for physics, and the application or utilization of machine learning for transformative advancements in science and technology. Encompassing materials, fusion science, high-energy physics, bioscience, and other diverse phenomena, along with digital twin and AI technology, the approach aims to explore the synergies emerging at the intersection of these fields. The symposium serves as a dynamic platform for interdisciplinary collaboration, allowing experts from diverse scientific backgrounds to collectively advance our understanding of various phenomena in the world and unravel mysteries through the use of simulations and machine learning.

Symposium 2 : Visualization and/or XR for data analysis

OrganizerNobuaki Ohno (Univ. of Hyogo)
Co-organizerHiroaki Ohtani (NIFS), Yuichi Tamura (Konan Univ.)
ObjectiveVisualization makes numerical data understandable to humans using computer graphics (CG), and virtual reality (VR) devices have been employed to enhance its efficiency. We focus on visualization and its related techniques, including the application of machine learning, for various types of numerical data, such as results from computer simulations, observational data, social science data, and other topics related to extended reality (XR). The goal of this symposium is to present state-of-the-art results related to visualization, XR, and associated technologies, and to facilitate a forum for exchanging ideas and discussing recent developments in these fields.