activity
20162022
most citedQuantitative characterization of surface topography using spectral analysis

507 citations · 668 across the 9 of their papers we have counts for

collaborators

9 papers

math.NA2022

Non-convex, ringing-free, FFT-accelerated solver using an incremental approximate energy functional

Ali Falsafi, Richard J. Leute, Martin Ladecký +1

Fourier-accelerated micromechanical homogenization has been developed and applied to a variety of problems, despite being prone to ringing artifacts. In addition, the majority of F…

math.NA2022★ 1 cited

Optimal FFT-accelerated Finite Element Solver for Homogenization

Martin Ladecký, Richard J. Leute, Ali Falsafi +4

We propose a matrix-free finite element (FE) homogenization scheme that is considerably more efficient than generic FE implementations. The efficiency of our scheme follows from a…

cond-mat.mtrl-sci2022★ 7 cited

contact.engineering -- Create, analyze and publish digital surface twins from topography measurements across many scales

Michael C. Röttger, Antoine Sanner, Luke A. Thimons +6

The optimization of surface finish to improve performance occurs largely through trial and error, despite significant advancements in the relevant science. There are three central…

cs.CE2021

Efficient topology optimization using compatibility projection in micromechanical homogenization

Indre Jödicke, Richard J. Leute, Till Junge +1

The adjoint method allows efficient calculation of the gradient with respect to the design variables of a topology optimization problem. This method is almost exclusively used in c…

math.NA2021★ 17 cited

Elimination of ringing artifacts by finite-element projection in FFT-based homogenization

Richard J. Leute, Martin Ladecký, Ali Falsafi +5

Micromechanical homogenization is often carried out with Fourier-accelerated methods that are prone to ringing artifacts. We here generalize the compatibility projection introduced…

physics.chem-ph2021★ 49 cited

Efficient implementation of atom-density representations

Félix Musil, Max Veit, Alexander Goscinski +5

Physically-motivated and mathematically robust atom-centred representations of molecular structures are key to the success of modern atomistic machine learning (ML) methods. They l…