Resistor Network
Resistor networks are being actively investigated as energy-efficient computing platforms, particularly for machine learning applications. Current research focuses on developing fast and scalable algorithms for simulating both linear and nonlinear networks, including novel approaches based on quadratic programming and spectral graph learning techniques. These advancements are crucial for overcoming computational bottlenecks in training larger networks and enabling the development of more sophisticated applications, such as soft robotics sensors and efficient circuit design. The ability to accurately model and learn these networks holds significant promise for improving energy efficiency in computing and advancing the design of novel sensing and control systems.