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 ...

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 ...

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 ...

Developing faster algorithms is an important but elusive goal for data scientists. The ability to accelerate complex computing tasks and reduce latency has far-reaching ramifications in areas such as ...

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 ...

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 ...

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 ...