As chemical engineering curricula increasingly integrate computational tools, the traditional acid-base titration lab is ...

We present a machine learning method based on random projections with Johnson-Lindenstrauss (JL) and/or Rahimi and Recht (2007) Random Fourier Features (RFFN) for efficiently learning linear and ...

Abstract: This paper develops several new dynamical designs, based on the gradient neural network (GNN), from the perspective of control theory to solve the time-varying Sylvester equation (TVSE). We ...

Nearly 200 years ago, the physicists Claude-Louis Navier and George Gabriel Stokes put the finishing touches on a set of equations that describe how fluids swirl. And for nearly 200 years, the ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Reported in ...

Zach began writing for CNET in November, 2021 after writing for a broadcast news station in his hometown, Cincinnati, for five years. You can usually find him reading and drinking coffee or watching a ...

Zeroing neural network (ZNN) is viewed as an effective solution to time-varying nonlinear equation (TVNE). In this paper, a further study is shown by proposing a novel combined discrete-time ZNN ...

In today’s fast-paced, ever-evolving world, the complexity of challenges faced in both business and everyday life requires a shift in how we approach problem solving. Traditional linear thinking, ...

Abstract: Riccati matrix equation (RME), a critical nonlinear matrix equation in autonomous driving and deep learning. However, memory-compute separation in traditional solving systems leads to ...