The explosion of real-time data arising from devices that sense and control the physical world requires improving synergy in research areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in the tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking the foundations of the discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data-driven control and optimization of dynamical processes. Our conference has been building a new community of people who think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area.

We invite submissions of papers addressing topics including:

Foundations of learning of dynamics models

System identification

Optimization for machine learning

Data-driven optimization for dynamical systems

Distributed learning over distributed systems

Reinforcement learning for physical systems

Safe reinforcement learning and safe adaptive control

Statistical learning for dynamical and control systems

Bridging model-based and learning-based dynamical and control systems

Machine learning for reduced-order modeling and physics-constrained systems

Physical learning in dynamical and control systems applications in robotics, autonomy, biology, energy systems, transportation systems, cognitive systems, neuroscience, etc.

The conference is open to any topic on the interface between machine learning, control, and optimization; its primary goal is to address scientific and application challenges in real-time processes modeled by dynamical or control systems.