Science-guided Machine Learning for Forward, Inverse, and Control Problems


Scientific machine learning (SciML) is an interdisciplinary field that solves complex scientific problems by combining computational and algorithmic techniques with machine learning methods. This talk will cover the most recent developments in SciML. We will highlight the limitations of current methodologies and explore new ideas to address them. Several exemplar problems will be investigated, including optimal control for dynamical systems and inference on chaotic models. Following that, we look at how our methods might be used in applications such as climate modeling, robotics, and biology .

Slide Deck