scispace - formally typeset
I

Iosif B. Meyerov

Researcher at N. I. Lobachevsky State University of Nizhny Novgorod

Publications -  76
Citations -  1031

Iosif B. Meyerov is an academic researcher from N. I. Lobachevsky State University of Nizhny Novgorod. The author has contributed to research in topics: Computer science & Xeon. The author has an hindex of 16, co-authored 70 publications receiving 842 citations. Previous affiliations of Iosif B. Meyerov include Saratov State University.

Papers
More filters
Journal ArticleDOI

Extended particle-in-cell schemes for physics in ultrastrong laser fields: Review and developments

TL;DR: A modified event generator is proposed that precisely models the entire spectrum of incoherent particle emission without any low-energy cutoff, and which imposes close to the weakest possible demands on the numerical time step.
Journal ArticleDOI

Ultrabright GeV Photon Source via Controlled Electromagnetic Cascades in Laser-Dipole Waves

TL;DR: In this paper, the authors proposed a new source concept based on a controlled interplay between the cascade and anomalous radiative trapping, and demonstrated that the concept becomes feasible at laser powers of around 7 PW, which is accessible at soon-to-be available facilities.
Journal ArticleDOI

Ultra-bright GeV photon source via controlled electromagnetic cascades in laser-dipole waves

TL;DR: In this paper, the authors proposed a directed GeV photon source, enabled by a controlled interplay between the cascade and anomalous radiative trapping, using advanced 3D QED particle-in-cell simulations and analytic estimates.
Journal ArticleDOI

Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization

TL;DR: Results on the urban water demand data in Milan prove that forecasting error is significantly low and that preliminary clustering allows for further reducing error while also improving computational performances.
Journal ArticleDOI

Particle-in-Cell Laser-Plasma Simulation on Xeon Phi Coprocessors

TL;DR: Step-by-step optimization techniques, such as improving data locality, enhancing parallelization efficiency and vectorization leading to an overall 4.2 x speedup on CPU and 7.5 x on Xeon Phi compared to the baseline version are demonstrated.