Speaker
Description
Machine learning and physics have long been deeply intertwined, and there have been eras when their relationship came to the forefront. Even in today’s revolutionary AI development, physics has played a significant role—for example, in diffusion models. From a physics standpoint as well, an integrative perspective across various specialized domains is provided by innovative new mathematical frameworks, and machine learning serves as one such framework. Launched in fiscal year 2022, the Scientific Transformation Area Research (A) initiative “Creation of Machine Learning Physics” was established to forge a new interdisciplinary field merging machine learning and physics. Now in its third year, it has produced a wide range of research outcomes and functions as a central hub where many researchers gather. In this talk, I will introduce the goals of this initiative, illustrating them with specific research examples, and discuss the future relationship between machine learning and physics.
