Scientific Machine Learning (SciML) is an emerging interdisciplinary field of research that integrates traditional scientific disciplines with machine learning methods to solve complex scientific problems. The primary goal of SciML is to enable researchers to leverage the power of machine learning to accelerate scientific discovery and to build predictive models that can simulate and optimize complex scientific systems. Scientific machine learning methods can be applied to a wide range of scientific fields, including applied mathematics, physics, chemistry, biology, geoscience, climate science, and materials science, among others.

The purpose of this webinar is to bring together researchers and practitioners from various fields to exchange knowledge and ideas on the latest advances in machine learning and its application in scientific research.

Organizers: Ming-Chih Lai (賴明治)  Ming-Cheng Shiue (薛名成) Te-Sheng Lin (林得勝)