NSF Award
2312502
Many real-world domains, spanning physical systems, social systems, brain networks, and epidemic networks, can be conceptualized as multi-agent dynamic systems, wherein different agents interact with each other and progress according to specific dynamics. Understanding and modeling these systems can enhance our comprehension of their underlying mechanisms, allowing us to make more accurate long-term predictions and better-informed decisions, with or without interventions. Despite extensive study of multi-agent dynamical systems in specific domains, there is currently no general solution available, and even the most knowledgeable experts may struggle to describe them mathematically. The proposed VIRTUALLAB framework aims to create a virtual lab capable of learning system dynamics from observed data, predicting future agent trajectories, and accurately forecasting potential system outcomes under a range of interventions. This project will facilitate the rapid adoption of AI techniques in different domains, promoting the digital revolution and the use of AI for healthcare, science, and public policy. The investigators plan to incorporate educational activities into the research, offering students exciting opportunities to apply AI and ML in various domains such as biomedical research, material science, and public health. They will also widely disseminate their findings through publications, tutorials at various conferences, and collaborations with domain experts. <br/><br/>The project has identified several limitations in existing approaches to modeling and predicting multi-agent dynamical systems. Firstly, approaches are often domain specific, and there is a lack of general methodology to address the full range of dynamical systems. Secondly, most dynamical systems are defined by complex ordinary or partial differential equations that can be difficult or even impossible to devise. Thirdly, making predictions can be very time-consuming and may not be applicable to large-scale systems. Lastly, very little work has addressed the problem of causal inference in multi-agent systems. The VIRTUALLAB framework is designed to be transformative and address these challenges. Firstly, it will provide general solutions to model multi-agent dynamical systems across a broad spectrum of applications, where the dynamics can be learned from incomplete and irregular observational data from the same or related systems. This will involve addressing several challenges, such as modeling continuous dynamics from incomplete signals, designing models that capture high-order nonlinear dynamics, generalizing learned dynamics to new systems with few observations, and scaling models to handle large-scale systems and make training and inference efficient for real-world systems. Secondly, VIRTUALLAB will provide accurate predictions of potential outcomes after an intervention, either at the node or system level, by leveraging offline data. Doing so will involve handling both system- and node-level intervention and continuous-time dynamic intervention, rather than static intervention that may occur in the future. Lastly, the project will test and evaluate the proposed framework using several use cases, including functional brain networks, molecular dynamics, and epidemic dynamics.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
