A team of researchers from multiple institutions, including Regents Professor Ying-Cheng Lai, a faculty member in the School of Electrical, Computer and Energy Engineering, have secured a $9 million Multidisciplinary University Research Initiatives, or MURI, award from the Office of Naval Research.
Led by Yale University John C. Malone Professor of Applied Physics Hui Cao, the team of thought leaders strives to better understand how complex, high-dimensional interventions can be used to manage or design physical systems that behave coherently.
The concept behind this initiative lies within an existing, counterintuitive and complicated occurrence witnessed in numerous complex systems: the introduction of disorder can yield more orderly behavior.
Collectively, the team believes that emerging techniques in machine learning offer an effective way to design these complex, disorderly interventions, including systems where traditional knowledge and existing analysis are ineffective. Ultimately, this introduction of “disorder” into physical systems aims to generate stability through recognition so that it can be corrected or accounted for using AI-driven techniques.
The core challenge focuses on the development of laser systems by which individual lasers are synchronized into the same phase, enabling them to operate effectively as a singular laser. If achievable, the process has the potential to generate powerful, controllable laser systems with applications in areas like directed energy and adaptive remote sensing.
“The goal is to synchronize hundreds and even thousands of lasers,” Lai says. “Each individual laser is small and produces little power, but if synchronized, you can produce lots of power in a compact size.”
The overarching task of developing and deploying AI-driven techniques for controlling complex systems using intervention could have impactful societal implications in fields like robotics and computer networks. It could give way to new methodologies for automated scientific experiments that are currently difficult to control.
“The research scope is broad with many disciplines so one individual would not be able to accomplish this,” Lai says. “Everybody involved was selected for unique expertise important to the project, and we put the expertise together to form a coherent team.”
The first step of the project will involve developing a theoretical model and demonstrating if the effort of synchronizing an array of lasers is feasible. With an extensive background in nonlinear dynamics and complex systems, Lai’s contributions to the project will be building AI-based computational models for synchronizing complex systems that ideally deliver a general principle for experimentalism for the team’s use moving forward.
Additionally, Lai’s work in improving machine learning strategies and interdisciplinary approaches to complex problem-solving will be beneficial to this collaborative process.