2 citations · 2 across the 5 of their papers we have counts for
6 papers
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
Mathieu Luisier, Nicolas Vetsch, Alexander Maeder +8
The Non-equilibrium Green's function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo…
Parallel Quadratic Selected Inversion in Quantum Transport Simulation
Vincent Maillou, Matthias Bollhofer, Olaf Schenk +2
Driven by Moore's Law, the dimensions of transistors have been pushed down to the nanometer scale. Advanced quantum transport (QT) solvers are required to accurately simulate such…
Accelerated Spatio-Temporal Bayesian Modeling for Multivariate Gaussian Processes
Lisa Gaedke-Merzhäuser, Vincent Maillou, Fernando Rodriguez Avellaneda +5
Multivariate Gaussian processes (GPs) offer a powerful probabilistic framework to represent complex interdependent phenomena. They pose, however, significant computational challeng…
Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance
Nicolas Vetsch, Alexander Maeder, Vincent Maillou +7
Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quant…
Mass-Spring Models for Passive Keyword Spotting: A Springtronics Approach
Finn Bohte, Theophile Louvet, Vincent Maillou +1
Mechanical systems played a foundational role in computing history, and have regained interest due to their unique properties, such as low damping and the ability to process mechan…
Serinv: A Scalable Library for the Selected Inversion of Block-Tridiagonal with Arrowhead Matrices
Vincent Maillou, Lisa Gaedke-Merzhaeuser, Alexandros Nikolaos Ziogas +2
The inversion of structured sparse matrices is a key but computationally and memory-intensive operation in many scientific applications. There are cases, however, where only partic…