ERI: Harnessing Adaptive Critical Oscillator Dynamics for Mechano-Intelligence

This Engineering Research Initiation (ERI) grant investigates how engineered systems can use their own physical dynamics to sense, learn, and respond efficiently. Conventional artificial intelligence often relies on energy-intensive digital computation, while many mechanical and physical systems naturally exhibit rich behaviors that can store and process information. The research will explore how adaptive nonlinear oscillators can operate near transitions between distinct patterns of motion, where they may become especially sensitive, flexible, and useful for computation. The resulting knowledge may support new forms of energy-efficient artificial intelligence embedded directly within machines, structures, and other engineered systems. Potential applications include adaptive robotics, structural and environmental monitoring, smart infrastructure, and autonomous sensing. By advancing the fundamental understanding of intelligent engineered systems, the project supports the national interest through progress in engineering, increased economic competitiveness, more resilient infrastructure, and technologies relevant to national defense. The project will also train a doctoral student in nonlinear dynamics, adaptive control, and physical computing, contributing to the development of a skilled engineering workforce in emerging areas of intelligent systems. The technical goal is to develop a fundamental understanding of critical dynamics in adaptive nonlinear systems whose parameters and dynamical landscape evolve over time, and to evaluate their potential for physical information processing. The project will develop adaptive mechanisms that adjust system parameters to regulate proximity to dynamical transitions and will investigate how these evolving dynamics affect sensitivity, memory, robustness, and computational performance. Mathematical modeling, stability analysis, numerical continuation, and computational simulations will be used to identify governing principles, performance limits, and common behaviors across representative nonlinear oscillators, including bistable and self-sustained systems. Experimental studies will implement these concepts in nonlinear mechanical beams using adaptive control, laser-based vibration measurements, strain sensing, and high-speed imaging. The research will then examine how networks of these oscillators can perform memory, temporal signal processing, pattern recognition, filtering, classification, and prediction of complex signals. Performance will be compared with conventional fixed-parameter and adaptive oscillator approaches to determine whether criticality-tracking mechanisms improve robustness, computational capacity, and energy efficiency under changing inputs and operating conditions. The anticipated contribution is a unified foundation for designing physical systems whose evolving dynamics provide both mechanical function and computation. 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.