In this article, we propose and analyse Particle-In-Cell (PIC) methods embedding sparse
grid reconstructions such as those introduced in Ricketson and Cerfon [Plasma Phys …

The accurate but fast calculation of molecular excited states is still a very challenging topic.
For many applications, detailed knowledge of the energy funnel in larger molecular …

Abstract Machine learning (ML) provides access to fast and accurate quantum chemistry
(QC) calculations for various properties of interest such as excitation energies. It is often the …

We propose a sparse grid-based adaptive noise reduction strategy for electrostatic particle-
in-cell (PIC) simulations. By projecting the charge density onto sparse grids we reduce the …

Grid-based simulations of hot fusion plasmas are often severely limited by computational
and memory resources; the grids live in four-to six-dimensional space and thus suffer the …

Substantial modifications of both the choice of the grids, the combination coefficients, the
parallel data structures and the algorithms used for the combination technique lead to …

Ultra-large–scale simulations via solving partial differential equations (PDEs) require very
large computational systems for their timely solution. Studies shown the rate of failure grows …

Massively parallel simulations of plasma microturbulence using GENE are facing the curse
of dimensionality, since the discretization of the five-dimensional gyrokinetic equations …

The accurate but fast calculation of molecular excited states is still a very challenging topic.
For many applications, detailed knowledge of the energy funnel in larger molecular …

The solution of high-dimensional problems, especially high-dimensional partial differential
equations (PDEs) that require the joint discretization of more than the usual three spatial …