Related papers: Integration of Machine Learning-Based Plasma Accel…
Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning…
Three-dimensional Particle-in-Cell (PIC) simulations with the code QuickPIC are used to illustrate the typical accelerating structures associated with the interaction of an intense laser beam with an underdense plasma in the blowout regime.…
Plasma Wakefield Acceleration represents one of the most promising techniques able to overcome the limits of conventional RF technology and make possible the development of compact accelerators. With respect to the laser-driven schemes, the…
Plasma-based accelerators are compact and provide high gradients, yet their practical use has been limited by energy gain, stability, beam quality, and energy transfer efficiency. Here, we address several of these challenges simultaneously…
This paper introduces the MAESTRO workflow, that enables the coupling of the PORTALS framework [P. Rodriguez-Fernandez et al, Nucl. Fusion 2024] with external solvers for the plasma equilibrium, pedestal physics, divertor constraints and…
Plasma accelerators driven by intense laser or particle beams provide gigavolt-per-meter accelerating fields, promising to drastically shrink particle accelerators for high-energy physics and photon science. Applications such as linear…
Meshfree simulation methods are emerging as compelling alternatives to conventional mesh-based approaches, particularly in the fields of Computational Fluid Dynamics (CFD) and continuum mechanics. In this publication, we provide a…
Geant4 is an object-oriented toolkit for the simulation of the passage of particles through matter. Its development was initially motivated by the requirements of physics experiments at high energy hadron colliders under construction in the…
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range…
Over the last ten years, the popularity of Machine Learning (ML) has grown exponentially in all scientific fields, including particle physics. The industry has also developed new powerful tools that, imported into academia, could…
Plasma accelerators can sustain very high acceleration gradients. They are promising candidates for future generations of particle accelerators for several scientific, medical and technological applications. Current plasma based…
In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization.…
The interaction of two lasers with a difference frequency near that of the ambient plasma frequency produces beat waves that can resonantly accelerate thermal electrons. These beat waves can be used to drive electron current and thereby…
Plasma waves generated in the wake of intense, relativistic laser or particle beams can accelerate electron bunches to giga-electronvolt (GeV) energies in centimetre-scale distances. This allows the realization of compact accelerators…
Three dimensional Particle in Cell simulations of Laser Wakefield Acceleration require a considerable amount of resources but are necessary to have realistic predictions and to design future experiments. The planned experiments for the…
Physical based simulations can be very time and computationally demanding tasks. One way of accelerating these processes is by making use of data-driven surrogate models that learn from existing simulations. Ensembling methods are…
We explore a novel simulation route for Plasma Wakefield Acceleration (PWFA) by using the computational method known as the Lattice Boltzmann Method (LBM). LBM is based on a discretization of the continuum kinetic theory while assuring the…
In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning…
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-scale, diverse, and standardized datasets for physics-based…
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of…