arXiv subjects

Giovanni Lapenta

Publications and source records attributed to Giovanni Lapenta.

At least 19 recordsLinked to original sources

arXiv2026

Electron neural closure for turbulent magnetosheath simulations: energy channels

In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a Fully Convolutional Neural Network (FCNN). Electron pressure plays an important role in generalized Ohm's law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit Particle-in-Cell simulation of decaying magnetosheath turbulence. We achieve this by training FCNN on a representative set of simulations with a smaller number of particles per cell and showing that our results generalise to a simulation with a large number of particles per cell. We evaluate the statistical properties of the learned equation of state, with a focus on pressure-strain interaction, which is crucial for understanding energy channels in turbulent plasmas. The resulting equation of state learned via FCNN significantly outperforms local closures, such as those learned by Multi-Layer Perceptron (MLP) or double adiabatic expressions. We report that the overall spatial distribution of pressure-strain and its conditional averages are reconstructed well. However, some small-scale features are missed, especially for the off-diagonal components of the pressure tensor. Nevertheless, the results are substantially improved with more training data, indicating favorable scaling and potential for improvement, which will be addressed in future work.

physics.plasm-ph↗

arXiv2025

Benchmark for two-dimensional large scale coherent structures in partially magnetized ExB plasmas -- Community collaboration & lessons learned

Low-temperature plasmas are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a vital tool for uncovering new physical phenomena and guiding technological development. Code benchmarking remains crucial for verifying implementations and evaluating performance. This work continues the Landmark benchmark initiative, a series specifically designed to support the verification of low-temperature plasma codes. In this study, seventeen simulation codes from a collaborative community of nineteen international institutions modeled a partially magnetized ExB Penning discharge. The emergence of large scale coherent structures, or rotating plasma spokes, endows this configuration with an enormous range of time scales, making it particularly challenging to simulate. The codes showed excellent agreement on the rotation frequency of the spoke as well as key plasma properties, including time-averaged ion density, plasma potential, and electron temperature profiles. Achieving this level of agreement came with challenges, and we share lessons learned on how to conduct future benchmarking campaigns. Comparing code implementations, computational hardware, and simulation runtimes also revealed interesting trends, which are summarized with the aim of guiding future plasma simulation software development.

physics.plasm-ph↗

arXiv2025

Polywell Revisited

The Polywell fusion concept, originally proposed by Robert W. Bussard in 1985, has been investigated for over four decades as a potential solution for achieving net fusion energy in a compact and economically viable reactor. It combines two distinct approaches: high-beta magnetic cusp confinement of electrons using polyhedral coil configurations and electrostatic ion confinement via a potential well formed by injected electron beams. While the hybrid nature of the Polywell system offers advantages in plasma stability and engineering simplicity, previous efforts have been limited by persistent challenges in achieving sufficient plasma confinement required to generate a net energy gain. In this study, we examine previous work and identify limitations of several Polywell embodiments that have historically impeded progress. We present an updated Polywell physics model incorporating experimental findings and recent first-principles particle-in-cell simulations. This updated model outlines a credible path toward overcoming confinement losses and achieving net energy gain using deuterium-tritium (D-T) fuels. Our findings provide a renewed scientific basis for the continued development of the Polywell fusion concept as a practical and scalable approach to fusion energy.

physics.plasm-ph↗

arXiv2025

Clustering Wind data at 1 AU to contextualize magnetic reconnection in the solar wind

Context. Magnetic reconnection events are frequently observed in the solar wind. Understanding the patterns and structures within the solar wind is crucial to put observed magnetic reconnection events into context, since their occurrence rate and properties are likely influenced by solar wind conditions. Aims. We employed unsupervised learning techniques such as self-organizing maps (SOM) and K-Means to cluster and interpret solar wind data at 1 AU for an improved understanding of the conditions that lead to magnetic reconnection in the solar wind. Methods. We collected magnetic field data and proton density, proton temperature, and solar wind speed measurements taken by the Wind spacecraft. After preprocessing the data, we trained a SOM to visualize the high-dimensional data in a lower-dimensional space and applied K-Means clustering to identify distinct clusters within the solar wind data. Results. Our analysis revealed that the reconnection events are distributed across five different clusters: a) slow solar wind, b) compressed slow wind, c) highly Alfvénic wind, d) compressed fast wind, and e) ejecta. Compressed slow and fast wind and ejecta are clusters associated with solar wind transients such as stream interaction regions and interplanetary coronal mass ejections. The majority of the reconnection events are associated with the slow solar wind, followed by the highly Alfvénic wind, compressed slow wind, and compressed fast wind, and a small fraction of the reconnection events are associated with ejecta. Conclusions. Unsupervised learning approaches with SOM and K-Means lead to physically interpretable solar wind clusters based on their transients and allow for the contextualization of magnetic reconnection exhausts' occurrence in the solar wind.

astro-ph.SR↗

arXiv2025

Particle-based Simulation of an Air-Breathing Electric Propulsion System

A novel concept called Air-Breathing Electric Propulsion proposes to fly satellites at altitudes in the range 180-250 km, since this would have some advantages for the performance of radio communication and Earth observation equipment. The ABEP satellites compensate the atmospheric drag through a continuous thrust provided by collecting, ionizing and accelerating the residual atmospheric particles. It is clear that the feasibility of this concept will require a significant design and testing effort, performed first on ground and later in orbit. Plasma simulation tools play a fundamental role in the development of this technology, for two main reasons: (i) they can potentially increase dramatically the optimization and testing process of ABEP systems, since on-ground testing and in-orbit demonstrators are costly and time consuming, and (ii) the fidelity of on-ground testing is limited by the finite size and pumping speed of high-vacuum facilities, as well as the means through which the orbital flow is produced. In this paper, we demonstrate a one-way coupled, particle-based simulation strategy for a CubeSat sized ABEP system. The neutral flow in the full geometry of the ABEP system comprising the intake and the thruster is simulated first through Direct Simulation Monte Carlo. Then, the resulting neutral density is used as the input for a Particle-in-Cell simulation of the detailed thruster geometry. The simulations are performed in 3D and within the VKI in-house code Pantera, taking advantage of the fully-implicit energy-conserving scheme.

physics.plasm-ph↗

arXiv2025

Bypassing the static input size of neural networks in flare forecasting by using spatial pyramid pooling

The spatial extension of active regions (ARs) of the Sun can vary from one case to the next. This is a problem when studying solar flares with Convolutional Neural Networks (CNNs) as they generally use input images of a fixed size. Different processes can be performed to retrieve a database with homogeneous-sized data, such as resizing. Unfortunately, key features can be lost or distorted during these processes. This can lead to a deterioration of the ability of CNNs to classify flares of different soft X-ray classes, especially those from ARs with complex structures. Our work aims to implement and test a CNN architecture that retains the full features of the original resolution of the input images. We compare the performance of two CNN architectures for solar flare prediction: the first is a traditional CNN with resized input whereas the other implements a spatial pyramid pooling (SPP) layer without any input resizing. Both are trained on the Spaceweather HMI Active Region Patch line-of-sight magnetogram database. We also study two cases of binary classification. In the first case, our model distinguishes ARs producing flares in less than 24h of class greater or equal to C1.0 from ARs producing flares in more than 24h or never; in the second case, it distinguishes ARs producing flares in less than 24h of class greater or equal to M1.0 from the other ARs. Our models implementing an SPP layer outperform the traditional CNN models when predicting flares greater or equal to C1.0 within 24h. However, their performances degrade sharply along the other models studied in this paper, when trained to classify images greater or equal to M1.0 flares. The degradation in SPP models when classifying only images greater or equal to M1.0 flares as positive may be attributed to its success in identifying features that appear in ARs a few hours before the flare, independently of their soft X-ray class.

astro-ph.SR↗

arXiv2024

Fully Kinetic Simulations of Proton-Beam-Driven Instabilities from Parker Solar Probe Observations

The expanding solar wind plasma ubiquitously exhibits anisotropic non-thermal particle velocity distributions. Typically, proton Velocity Distribution Functions (VDFs) show the presence of a core and a field-aligned beam. Novel observations made by Parker Solar Probe (PSP) in the innermost heliosphere have revealed new complex features in the proton VDFs, namely anisotropic beams that sometimes experience perpendicular diffusion. In this study, we use a 2.5D fully kinetic simulation to investigate the stability of proton VDFs with anisotropic beams observed by PSP. Our setup consists of a core and an anisotropic beam populations that drift with respect to each other. This configuration triggers a proton-beam instability from which nearly parallel fast magnetosonic modes develop. Our results demonstrate that before this instability reaches saturation, the waves resonantly interact with the beam protons, causing perpendicular heating at the expense of the parallel temperature.

astro-ph.SR↗

arXiv2024

Anisotropic Heating and Parallel Heat Flux in Electron-only Magnetic Reconnection with Intense Guide Fields

Electron-only reconnection is a process recently observed in the Earth's magnetosheath, where magnetic reconnection occurs at electron kinetic scales with only electrons involved. Electron-only reconnection is likely to have a significant impact on the energy conversion and dissipation of turbulence cascades at kinetic scales. This paper investigates electron-only reconnection under different intensities of strong guide fields via two-dimensional fully kinetic Particle-in-Cell (PIC) simulations, focusing on electron heating. The simulations are initialized with a force-free current sheet equilibrium under various intensities of strong guide fields. The quadrupolar structures of the out-of-plane magnetic field and asymmetric structures of density are found to become narrower in space as the guide field increases. Electron velocities are considerably larger than ion velocities, and the motion of both species is notably affected by the strength of guide fields. Similarly to previous experiments studies, electron temperature anisotropy along separatrices is observed, which is found to be mainly caused by the variations of parallel temperature. Both regions of anisotropy and parallel temperature increase/decrease along separatrices become thinner with increasing guide fields. Besides, we find a transition from a quadrupolar to a six-polar to an eight-polar structure in temperature anisotropy and parallel temperature as the guide field intensifies. Non-Maxwellian electron velocity distribution functions (EVDFs) at different locations in the three simulations are observed. Our results show that parallel electron velocity varies notably with different guide field intensities and strong parallel electron heat flux is observed.

physics.plasm-ph↗

arXiv2023

Exactly energy-conserving electromagnetic Particle-in-Cell method in curvilinear coordinates

In this paper, we introduce and discuss an exactly energy-conserving Particle-in-Cell method for arbitrary curvilinear coordinates. The flexibility provided by curvilinear coordinates enables the study of plasmas in complex-shaped domains by aligning the grid to the given geometry, or by focusing grid resolution on regions of interest without overresolving the surrounding, potentially uninteresting domain. We have achieved this through the introduction of the metric tensor, the Jacobian matrix, and contravariant operators combined with an energy-conserving fully implicit solver. We demonstrate the method's capabilities using a Python implementation to study several one- and two-dimensional test cases: the electrostatic two-stream instability, the electromagnetic Weibel instability, and the geomagnetic environment modeling (GEM) reconnection challenge. The test results confirm the capability of our new method to reproduce theoretical expectations (e.g. instability growth rates) and the corresponding results obtained with a Cartesian uniform grid when using curvilinear grids. Simultaneously, we show that the method conserves energy to machine precision in all cases.

physics.plasm-ph↗

arXiv2023

Generation of sub-ion scale magnetic holes from electron shear flow instabilities in plasma turbulence

Magnetic holes (MHs) are coherent structures associated with strong magnetic field depressions in magnetized plasmas. They are observed in many astrophysical environments at a wide range of scales but their origin is still under debate. In this work we investigate the formation of sub-ion scale MHs using a fully kinetic 2D simulation of plasma turbulence initialized with parameters typical of the Earth's magnetosheath. Our analysis shows that the turbulence is capable of generating sub-ion scale MHs from large scale fluctuations via the following mechanism: first, the nonlinear large scale dynamics spontaneously leads to the development of thin and elongated electron velocity shears; these structures then become unstable to the electron Kelvin-Helmholtz instability and break up into small scale electron vortices; the electric current carried by these vortices locally reduces the magnetic field, inducing the formation of sub-ion scale MHs. The MHs thus produced exhibit features consistent with satellite observations and with previous numerical studies. We finally discuss the kinetic properties of the observed sub-ion scale MHs, showing that they are characterized by complex non-Maxwellian electron velocity distributions exhibiting anisotropic and agyrotropic features.

physics.plasm-ph↗

arXiv2023

Advances in the Implementation of the Exactly Energy Conserving Semi-Implicit (ECsim) Particle in Cell Method

The Energy Conserving semi-implicit method (ECsim), presented by Lapenta in 2017, is a Particle in Cell (PIC) algorithm for the simulation of plasmas. Energy conservation is achieved within a semi-implicit formulation that does not require any non-linear solver. A mass matrix is introduced to express linearly the particle-field coupling. With the mass matrix the algorithm preserves energy conservation to machine precision. The construction of the mass matrix is the central nature of the method and also the main cost of the computational cycle. We analyze here three methods that modify the construction of the mass matrix. First, we consider how the sub-cycling of the particle motion modifies the mass matrix. Second, we introduce a form of smoothing that reduces the noise while retaining exact energy conservation. Finally, we discuss an approximation of the mass matrix that transform the ECsim scheme to the implicit moment method.

physics.plasm-ph↗

arXiv2022

Spectral properties and energy transfer at kinetic scales in collisionless plasma turbulence

By means of a fully kinetic simulation of freely decaying plasma turbulence, we study the spectral properties and the energy exchanges characterizing the turbulent cascade in the kinetic range. We find that the magnetic field spectrum follows the ${k^{-α}\,exp(-λ\, k)}$ law at kinetic scales with $α\!\simeq\!2.73$ and $λ\!\simeq\!ρ_e$ (where ${ρ_e}$ is the electron gyroradius). The same law with $α\!\simeq\!0.94$ and an exponential decay characterized by $λ\!\simeq\!0.87ρ_e$ is observed in the electron velocity spectrum but not in the ion velocity spectrum that drops like a steep power law $\sim k^{-3.25}$ before reaching electron scales. By analyzing the filtered energy conversion channels, we find that the electrons play a major role with respect to the ions in driving the magnetic field dynamics at kinetic scales. Our analysis reveals the presence of an indirect electron-driven mechanism that channels the e.m. energy from large to sub-ion scale more efficiently than the direct nonlinear scale-to-scale transfer of e.m. energy. This mechanism consists of three steps: in the first step the e.m. energy is converted into electron fluid flow energy at large scales; in the second step the electron fluid flow energy is nonlinearly transferred towards sub-ion scales; in the final step the electron fluid flow energy is converted back into e.m. energy at sub-ion scales. This electron-driven transfer drives the magnetic field cascade up to fully developed turbulence, after which dissipation becomes dominant and the electrons start to subtract energy from the magnetic field and dissipate it via the pressure-strain interaction at sub-ion scales.

physics.plasm-ph↗

arXiv2022

2022 Review of Data-Driven Plasma Science

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.

physics.plasm-ph↗

arXiv2022

Anisotropic electron heating in turbulence-driven magnetic reconnection in the near-Sun solar wind

We perform a high-resolution two-dimensional fully-kinetic numerical simulation of a turbulent plasma system with observation-driven conditions, in order to investigate the interplay between turbulence, magnetic reconnection, and particle heating from ion to sub-electron scales in the near-Sun solar wind. We find that the power spectra of the turbulent plasma and electromagnetic fluctuations show multiple power-law intervals down to scales smaller than the electron gyroradius. Magnetic reconnection is observed to occur in correspondence of current sheets with a thickness of the order of the electron inertial length, which form and shrink due to interacting ion-scale vortexes. In some cases, both ion and electron outflows are observed (the classic reconnection scenario), while in others -- typically for the shortest current sheets -- only electron jets are presents ("electron-only reconnection"). At the onset of reconnection, the electron temperature starts to increase and a strong parallel temperature anisotropy develops. This suggests that in strong turbulence electron-scale coherent structures may play a significant role for electron heating, as impulsive and localized phenomena such as magnetic reconnection may transfer energy from the electromagnetic fields to particles more efficiently than damping mechanisms related to interactions with wave-like fluctuations.

physics.space-ph↗

arXiv2022

Identification of high order closure terms from fully kinetic simulations using machine learning

Simulations of large-scale plasma systems are typically based on a fluid approximation approach. These models construct a moment-based system of equations that approximate the particle-based physics as a fluid, but as a result lack the small-scale physical processes available to fully kinetic models. Traditionally, empirical closure relations are used to close the moment-based system of equations, which typically approximate the pressure tensor or heat flux. The more accurate the closure relation, the stronger the simulation approaches kinetic-based results. In this paper, new closure terms are constructed using machine learning techniques. Two different machine learning models, a multi-layer perceptron and a gradient boosting regressor, synthesize a local closure relation for the pressure tensor and heat flux vector from fully kinetic simulations of a 2D magnetic reconnection problem. The models are compared to an existing closure relation for the pressure tensor, and the applicability of the models is discussed. The initial results show that the models can capture the diagonal components of the pressure tensor accurately, and show promising results for the heat flux, opening the way for new experiments in multi-scale modeling. We find that the sampling of the points used to train both models play a capital role in their accuracy.

physics.plasm-ph↗

arXiv2022

Do we need to consider electron kinetic effects to properly model a planetary magnetosphere: the case of Mercury

The magnetosphere of Mercury is studied using an implicit full particle in cell simulation (PIC). We use a hybrid simulation where ions are full particles and electrons are considered as a fluid to start a full PIC simulation where electrons are also particles and follow their distribution function. This approach allows us to estimate the changes introduced by the electron kinetic physics. We find that the overall macroscopic state of the magnetosphere of Mercury is little affected but several physical processes are significantly modified in the full PIC simulation: the foreshock region is more active with more intense shock reformation, the Kelvin-Helmholtz rippling effects on the nightside magnetopause are sharper, and the magnetotail current sheet becomes thinner than those predicted by the hybrid simulation. The greatest effect of the electron physics, comes from the processes of particle energization. Both species, not just the electrons, are found to gain more energy when kinetic electron processes are taken into account. The region with the most energetic plasma is found on the dusk side of the tail where magnetic flux ropes are formed due to reconnection. We find that the ion and electron energization is associated with the regions of reconnection and the development of kinetic instabilities caused by counter-streaming electron populations. The resulting electron distributions are highly non Maxwellian, a process that neither MHD nor hybrid models can describe.

astro-ph.EP↗

arXiv2021

Kinetic heating by Alfvén waves in magnetic shears

With first-principles kinetic simulations, we show that a large-scale Alfvén wave (AW) propagating in an inhomogeneous background decays into kinetic Alfvén waves (KAWs), triggering ion and electron energization. We demonstrate that the two species can access unequal amounts of the initial AW energy, experiencing differential heating. During the decay process, the electric field carried by KAWs produces non-Maxwellian features in the particle VDFs, in accordance with space observations. The process we present solely requires the interaction of a large-scale AW with a magnetic shear and may be relevant for several astrophysical and laboratory plasmas.

physics.plasm-ph↗

arXiv2021

Unsupervised classification of simulated magnetospheric regions

In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with Principal Component Analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with K-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

physics.space-ph↗