Information theory and computation

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction

2024-03-20 11:29 AM by Wanda Parisien

Classical reservoir computing We start with a nonlinear dynamical network of N variables of the following general form, $$\dot{{{{{{{{\bf{x}}}}}}}}}(t)={{{{{{{\boldsymbol{f}}}}}}}}[{{{{{{{\bf{x}}}}}}}}(t)],$$ (1) where ${{{{{{{\bf{x}}}}}}}}(t)={[{x}_{1}(t),\ldots,{x}_{N}(t)]}^{\top }$ denotes the N-dimensional (N-D) state of the system at time t, and ${{{{{{{\boldsymbol{f}}}}}}}}[{{{{{{{\bf{x}}}}}}}}(t)]={\left({f}_{1}[{{{{{{{\bf{x}}}}}}}}(t)],{f}_{2}[{{{{{{{\bf{x}}}}}}}}(t)],\ldots,{f}_{N}[{{{{{{{\bf{x}}}}}}}}(t)]\right)}^{\top }$ is the N-D nonlinear vector field. In this article, we assume that neither the vector field f (equivalently, …

Universal scaling between wave speed and size enables nanoscale high-performance reservoir computing based on propagating spin-waves

2024-03-01 03:31 AM by Wanda Parisien

Physical system of a magnetic device We consider a magnetic device of a thin rectangular system with cylindrical injectors (see Fig. 1c). The size of the device is L × L × D. Under the uniform external magnetic field, the magnetization is along the z direction. Electric current translated from time series data is injected at the Np injectors …

Solving real-world optimization tasks using physics-informed neural computing

2024-01-08 02:53 AM by Wanda Parisien

Optimization tasks are at the heart of many real-world applications across a variety of scientific disciplines, particularly in physics and engineering. From the seemingly simple task of swinging up a pendulum to the complex maneuver of a spacecraft swingby, these tasks demand a sophisticated and accurate understanding of various interconnected factors. At the core of …