Function-on-Function Regression for Trajectory Prediction of Small-Scale Particles towards Next-generation Neuromorphic Computing

Precise and efficient motion prediction and manipulation of micro- and nanoparticles in a complex fluid suspension system under external electric fields has the potential to revolutionize the manufacture of scalable functional nanodevices. However, the physical motion model of the particle based on physical simulation does not consider the effects in the complex fluid suspension system, e.g., boundary conditions, fluid motion, and particle interactions, and often results in imperfect prediction of particle trajectories under the coupled global field. This study proposes a data-driven approach for small-scale particle trajectory prediction by leveraging both physical simulation model and experimental data. Historical function-on-function regression is used to predict experimental trajectories from corresponding simulation trajectories. A gradient boosting algorithm is used for model estimation. Our study is the first-of-its-kind that uses historical function-on-function regression to demonstrate the efficacy of predicting experimental trajectories from simulation trajectories in small-scale particle manipulation under electrical fields, which eventually leads to the design of new automated processes for efficient and smart manufacturing of functional nanodevices towards next-generation neuromorphic computing.