GitHub

Finite basis physics-informed neural networks (FBPINNs)

This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...
IEEE

ZNNs With a Varying-Parameter Design Formula for Dynamic Sylvester Quaternion Matrix Equation

Abstract: This article aims to studying how to solve dynamic Sylvester quaternion matrix equation (DSQME) using the neural dynamic method. In order to solve the DSQME, the complex representation ...
IEEE

Unified Solution of Different-Kind Future Matrix Equations Using New Nine-Instant Discretization Formula and Zeroing Neural Dynamics

Abstract: In this article, time-varying matrix equation problems, including the Lyapunov equation, matrix inversion, and generalized matrix inversion are investigated in a future (or say, discrete ...
CNBC

Upstart chipmakers keep challenging Nvidia. This time it's Microsoft-backed D-Matrix

D-Matrix says its chips can run inference workloads 10 times faster and using five times less energy than a standalone graphics processing unit from Nvidia. Like Cerebras, D-Matrix is trying to prove ...