When Machine Learning meets Dynamical Systems: Theory and Applications

Abstract

The recent wave of using machine learning to analyze and manipulate real-world systems has inspired many research topics in the joint interface of machine learning and dynamical systems. However, the real world applications are diverse and complex with vulnerabilities such as simulation divergence or violation of certain prior knowledge. As ML-based dynamical models are implemented in real world systems, it generates a series of challenges including scalability, stability and trustworthiness. Through this workshop, we aim to provide an informal and cutting-edge platform for research and discussion on the co-development between machine models and dynamical systems. We welcome all the contributions related to ML based application/theory on dynamical systems and solution to ML problem from dynamical system perspective. From an alternative perspective, many machine learning problems can be viewed as dynamical systems, with examples ranging from neural network forward propagation to optimization dynamics and countless problems with sequential data. These generate increasing interest to study the intrinsic, evolving dynamics of these problems, with the potential to come up with novel methodologies for theory development and their applications. The mission of the MLmDS workshop is to bring together researchers from diverse backgrounds including but not limited to artificial intelligence and dynamical systems, gathering insights from these fields to facilitate collaboration and adaptation of theoretical and application knowledge amongst them.