Automated design of complex dynamic systems.

Area of Science:

• Integrates computational principles with the design of physical systems, spanning fields like robotics and photonics.
• Focuses on exploiting inherent nonlinear dynamics within physical systems for computational tasks.

Background:

• Current research aims to embed computations within physical systems to minimize external control efforts and enhance signal processing efficiency.
• Optimization of numerous system parameters (structural, material) is crucial but challenging due to variability.

Purpose of the Study:

• To apply machine learning algorithms for optimizing physical dynamic systems.
• To demonstrate the extension of machine learning to differential equations for parameter optimization.
• To design robust physical systems using machine learning training methodologies.

Main Methods:

• Utilized machine learning algorithms, typically applied to abstract computational entities, for optimizing parameters in physical dynamical systems.
• Extended machine learning to the field of differential equations to optimize system behavior parameters.
• Related the derived optimization method to direct collocation, a known technique in optimal control.

Main Results:

• Successfully applied machine learning to optimize parameters governing the behavior of physical dynamical systems.
• Demonstrated the effectiveness of machine learning training methodologies in creating robust systems.
• Provided examples using both simple and complex models of physical dynamical systems.

Conclusions:

• Machine learning algorithms can be effectively extended to optimize physical dynamic systems described by differential equations.
• The developed optimization method is closely linked to direct collocation in optimal control.
• Identified a significant unexplored overlap between machine learning and optimal control, paving the way for novel smart system design.