Abstract: Sparsity-regularized linear inverse problem has served as the base in many disciplines, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed ...

If you're a soccer fan, you're familiar with this common sight: A penalty kick is in place, with a "wall" of defenders lined up in front of the goal, ready to leap to try to block the ball if it sails ...

This Julia package aims at performing automatic bifurcation analysis of possibly large dimensional equations F(u, λ)=0 where λ is real by taking advantage of iterative methods, dense / sparse ...

Chemistry, mathematics and physics are central to our understanding of nature. Physics explores the fundamental laws of mechanics, electromagnetism, quantum mechanics and relativity. Chemistry studies ...

Abstract: The numerical solution of coupled partial differential equations (PDEs) represents a significant challenge for traditional methods such as the finite element method (FEM), particularly in ...

This paper proposes a family of line-search methods to deal with weighted orthogonal procrustes problems. In particular, the proposed family uses a search direction based on a convex combination ...

Thermoelectric generator (TEG) with improved performance is a promising technology in power supply and energy harvesting. Existing studies primarily adopt constant material properties to investigate ...

The consumption of solving large-scale linear equations is one of the most critical issues in numerical computation. An innovative method is introduced in this study to solve linear equations based on ...

In this study, we applied the variational iteration method to solve the Boussinesq time equation. Bossiness’s article from 1872 introduced the equations that are now known as the Boussinesq equations.

Dude, what if everything around us was just ... a hologram? The thing is, it could be—and a University of Michigan physicist is using quantum computing and machine learning to better understand the ...

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster. In high ...