2024/12/12 by Vrugt, Michael te · 1 citation
#Applied Physics (physics.app-ph) #Artificial Intelligence (cs.AI) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Quantum Physics (quant-ph)
paper · doi:10.48550/arxiv.2412.13212
There is a growing interest in the development of artificial neural networks that are implemented in a physical system. A major challenge in this context is that these networks are difficult to train since training here would require a change of physical parameters rather than simply of coefficients in a computer program. For this reason, reservoir computing, where one employs high-dimensional recurrent networks and trains only the final layer, is widely used in this context. In this chapter, I introduce the basic concepts of reservoir computing. Moreover, I present some important physical implementations coming from electronics, photonics, spintronics, mechanics, and biology. Finally, I provide a brief discussion of quantum reservoir computing.