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REVIEW 4 major objections 6 minor 1 cited by

Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Fully kinetic magnetosphere simulations scale to 32,768 APUs.

desk verdict Credible exascale scaling study for implicit PIC; the 'solves micro-macro coupling' physics claim is not supported by the evidence. read the letter →

arxiv 2507.20719 v1 pith:ZRWT2WEW submitted 2025-07-28 cs.CE

Stefano Markidis , Andong Hu , Ivy Peng , Luca Pennati , Ian Lumsden , Dewi Yokelson , Stephanie Brink , Olga Pearce
show 4 more authors
Thomas R.W. Scogland Bronis R. de Supinski Gian Luca Delzanno Michela Taufer
This is my paper
classification cs.CE
keywords implicitParticle-in-Cellglobalmagnetospheresimulationmicro-macrocouplingexascalecomputingMI300AAPUGaussianMixtureModelcompressionmagneticreconnectionparticlecoalescenceandsplitting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper aims to show that fully kinetic, implicit Particle-in-Cell (PIC) simulations can now model the global magnetospheres of small and medium planets, systems spanning 100 to 1,000 ion skin depths, with realistic solar wind parameters and with both electron-scale and global-scale physics resolved self-consistently. The authors report scaling the iPIC3D code to 32,768 AMD Instinct MI300A APUs on El Capitan, with weak-scaling efficiency near 79 percent at the largest size and a projected sustained performance of 22.4 PFLOP/s. If the central claim is right, the longstanding compromise of treating electrons as a fluid or embedding local kinetic patches in a fluid model is no longer the only practical route to global magnetospheric simulation. The result would open direct first-principles study of how magnetic reconnection and plasma turbulence emerge within realistic magnetospheric configurations rather than idealized setups.

What carries the argument

The central object is the moment-implicit PIC formulation in iPIC3D: particles are advanced with a relativistic predictor–corrector mover that includes a guiding-center correction, while the fields are updated from a moment-based Maxwell equation whose implicit dielectric tensor $\chi$ suppresses unresolved high-frequency modes through spectral compression and selective damping. This damping is what lets the method take ten times larger time steps and grid spacings and still remain stable and accurate. The supporting mechanisms are dynamic particle coalescence and splitting to keep subdomain particle counts balanced, a CPU–GPU workflow that overlaps the Maxwell solver with in-situ analytics, and GMM-based compression of velocity-space distribution functions.

What would settle it

Run the same 32,768-APU global magnetosphere simulation with hardware counters enabled on all nodes and compare the aggregate measured FLOP count with the projected 22.4 PFLOP/s; alternatively, on a smaller domain, run the implicit scheme at ten times the time step and grid spacing against a high-resolution explicit PIC reference and check whether reconnection rate, distribution functions, and onset timing agree within the claimed accuracy.

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Extended reading notes

Core claim

The central claim is that the implicit discretization of the Vlasov–Maxwell system removes the explicit-PIC stability limits on time step and grid spacing, permitting time steps and grid spacings up to ten times larger than explicit methods. In a three-dimensional simulation this lowers resolution requirements by a factor of about $10^4$ while still retaining electron-scale physics. Using that algorithmic headroom together with GPU/APU kernels, dynamic particle splitting and coalescence, and in-situ Gaussian Mixture Model compression, the paper demonstrates production runs whose domains span 100 to 1,000 ion skin depths, reaching the size regime of Mercury's and Ganymede's magnetospheres. The authors present this as a new modeling regime for space physics, in which global fully kinetic simulations with physically realistic parameters become computationally feasible.

Load-bearing premise

The load-bearing premise is that the implicit scheme's factor-of-ten relaxation of time step and grid spacing preserves the same physics as an explicit resolve-everything run, with that accuracy claim imported from earlier work rather than validated here, and that the headline 22.4 PFLOP/s figure, extrapolated from single-node profiling, faithfully represents the full 32,768-APU run.

Editorial extensions

If this is right

  • Small-to-medium planetary magnetospheres such as Mercury's and Ganymede's can be simulated fully kinetically with realistic parameters, including self-consistent bow shocks, magnetotails, and reconnection sites.
  • Magnetic reconnection and plasma turbulence, traditionally studied in idealized local configurations, can be examined inside self-consistent global field topologies rather than imposed Harris sheets.
  • The factor-of-ten relaxation of time step and grid spacing cuts the computational cost of 3D kinetic magnetosphere simulations by roughly four orders of magnitude relative to explicit PIC constraints.
  • The same implicit-PIC plus APU plus in-situ-compression workflow provides a reusable pattern for other exascale kinetic plasma simulations facing micro-macro coupling.
  • GMM-based compression stores electron and ion distribution functions at compression ratios above 1,000 with Jensen–Shannon divergence around $10^{-2}$, making full phase-space diagnostics practical at scale.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the scaling path shown here reaches 100 to 1,000 ion skin depths, but Earth's magnetopause is about 600 ion skin depths and its computational domain would need to be several times larger per dimension; reaching Earth-scale fully kinetic simulation appears to require further algorithmic advances, which the paper itself flags as future work.
  • Editorial inference: the demonstrated capability makes a testable prediction that a global fully kinetic run should reproduce known magnetotail reconnection signatures, such as plasmoid chains and characteristic ion distribution functions, without imposing an idealized initial current sheet; comparing those outputs with hybrid and MHD-embedded-PIC results would quantify the added value of full kin
  • Editorial inference: because the 22.4 PFLOP/s figure is extrapolated from a single-node rocprof FLOP count scaled by node count and measured parallel efficiency, the most directly verifiable observable for the capability claim is the measured time-to-solution of the 32,768-APU production run, which is not reported as a standalone number.
  • Editorial inference: the in-situ change-point detection on compressed distribution functions suggests a path toward automated, real-time identification of reconnection onset and particle heating in future magnetospheric simulations, but that extension is not established by the experiments reported here.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper reports scaling and optimization work for the iPIC3D implicit Particle-in-Cell code on AMD MI300A APUs, up to 32,768 APUs on El Capitan. It presents weak and strong scaling results, a hybrid CPU-GPU workflow, dynamic particle control, Gaussian-Mixture-Model compression, and in-transit analysis, and it claims that these capabilities make fully kinetic global magnetosphere simulations at 100-1,000 ion skin depths practical, thereby addressing micro-macro coupling in space physics. The headline performance number is a projected 22.4 PFLOP/s. The paper's evidence is overwhelmingly performance-oriented; the physical-accuracy claims that underlie the micro-macro coupling narrative are imported from prior work and are not validated in the presented production-scale simulation.

Significance. If the central claims are supported, this is a significant HPC contribution: a fully kinetic, global magnetosphere simulation at 100-1,000 ion skin depths would open a new modeling regime for small-to-medium planetary magnetospheres such as Mercury and Ganymede. The paper's strengths include multi-system scaling data on six architectures, a clearly labeled projected peak performance, a reproducible artifact repository, and a thoughtful integration of I/O, compression, and analysis into the simulation workflow. The scaling numbers are internally self-consistent, and the paper is appropriately careful to call the headline performance 'projected.' However, the physical claim that these runs preserve electron-scale physics and thereby resolve micro-macro coupling is not established by the evidence presented; the production run's grid spacing appears incompatible with resolving electron diffusion regions unless an unusual mass ratio is used, and no accuracy validation is reported.

major comments (4)
  1. [Section VI.A and Figure 9] The central micro-macro coupling claim is not established by the presented simulation. The production run shown in Figure 9 is 200 x 100 x 100 d_i on a 320 x 160 x 160 grid, giving Delta_x = 0.625 d_i. The paper does not report the ion-to-electron mass ratio. With a realistic ratio m_i/m_e = 1836, d_e = d_i/sqrt(1836) ~ 0.023 d_i, so Delta_x ~ 27 d_e; even with a strongly reduced ratio m_i/m_e = 100, Delta_x ~ 6 d_e. Collisionless reconnection's electron diffusion region is of order a few d_e, so the grid cannot resolve the electron-scale physics invoked in the abstract. The authors should either report the mass ratio and provide evidence that electron diffusion regions are resolved (e.g., a convergence study or a comparison against explicit PIC at higher resolution), or revise the physical claims to describe an ion-scale fully kinetic simulation with unresolved electron-scale diffusion regions.
  2. [Section V.A and Table IV] The 22.4 PFLOP/s 'sustained' performance is not a directly measured sustained rate. It is obtained by taking a single-node rocprof FLOP count, scaling by the number of nodes, and multiplying by measured parallel efficiency, and the 32,768-APU run was executed only once. The paper should report the per-node FLOP count used, the measured execution time of the largest run, and a sensitivity analysis or error bar for the extrapolation. Alternatively, the term 'sustained' should be replaced consistently with 'projected' in the abstract, Table IV, and Section V.A, and the 'unprecedented' wording should be tempered accordingly.
  3. [Section IV and V.A] The scaling benchmark is a GEM-inspired reconnection test, but it is used only for performance; no accuracy metric is reported. The statement that implicit grids and time steps 10x larger than explicit ones are usable 'without sacrificing physical accuracy' is imported from refs. [34,37,38] and is not re-established for the global 100-1,000 d_i regime. To make the physics claim testable, the paper should add at least one accuracy validation relevant to the production run, such as comparing reconnection rate, island growth, or distribution-function evolution at the Figure 9 resolution against a high-resolution explicit or implicit reference simulation.
  4. [Section V.A] The strong-scaling efficiency of the 'medium' simulation is reported to drop to approximately 39% at the highest APU counts, while the text and Figure 6 do not clearly show error bars or repeated runs for the largest scale. Since the largest run was executed once, the difference between the medium and large simulation curves at high node counts should be interpreted cautiously; please state the number of repetitions for each data point and add error bars where available.
minor comments (6)
  1. [Abstract and Section III.A] The abstract says time steps and grid spacing are 'up to 10x larger,' while Section III.A says 'at least 10x larger'; please make the wording consistent.
  2. [Figure 9 caption] The colorbar label appears garbled ('!e 0-2.1-1'), and the caption should specify the mass ratio, the grid spacing in units of d_e, and the final particle count after particle control.
  3. [Section V.A and VI.B] The text mentions a '33-trillion-particle run' while Figure 9's caption reports 'initially one billion particles'; clarify whether these refer to the same simulation and report the final particle count after splitting and coalescence.
  4. [Figure 7] The right panel shows parallel efficiency exceeding 100% for Lassen, and the text explains this as a network effect; please clarify whether this is super-linear speedup or a baseline artifact, and label the ideal-efficiency line.
  5. [Table II] The 'G#' symbol is not defined anywhere; please add a legend or footnote explaining its meaning.
  6. [References] Reference [43] is formatted inconsistently ('H. Andong'); please correct the author name and verify all reference metadata.

Circularity Check

0 steps flagged · score 2.0 of 10

No reductive circularity: the implicit-accuracy premise is imported from established external literature, and the headline FLOP/s figures are measurements/extrapolations, not predictions forced by fitted inputs.

full rationale

The paper's derivation chain is not circular in the sense defined here. The implicit discretization in Section III.A is presented through the moment-implicit field update (Eqs. 4-7), with the stability and accuracy properties attributed to Brackbill and Forslund ([37], [38]) and to the iPIC3D method paper ([34]); the 10x grid/timestep claim is an imported, parameter-free property of an established method, not a quantity fitted in this paper and later renamed a prediction. The performance claims are direct measurements: wall-clock scaling in Figures 6-8 and the Table IV FLOP/s projections obtained by rocprof per-node counts scaled by node count and measured parallel efficiency are an extrapolation, not a circular fit. The GMM compression is evaluated against generic compressors on a specific compression task, and the change-point analysis is demonstrated on simulation output; neither reduces to its own input. The main epistemic weakness is a validation gap, not circularity: the global run uses 0.625 d_i cells with no reported mass ratio or convergence study, so the assertion that electron-scale reconnection physics is preserved is inherited from prior literature rather than derived here. That is a correctness risk, not a circular reduction. Self-citations such as [34] and [43] are provenance for code components; they are not used as uniqueness theorems or as the sole support for load-bearing claims, and independent references [4], [37], [38] also carry the accuracy premise. Score 2 reflects the modest reliance on self-cited prior work while no equation or prediction reduces by construction to a fitted input.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on four main assumptions: the accuracy of implicit PIC at 10x resolution, the validity of the FLOP-scaling projection, the fidelity of GMM to velocity distributions, and the physical representativeness of the rendered simulation. The paper contributes engineering, not new physics, so the ledger is dominated by imported domain assumptions rather than free parameters.

free parameters (4)
  • particle control threshold theta = 0.05 (5%)
    Chosen by hand to trigger particle splitting or coalescence; a parameter of the load-balancing algorithm in Section III-B.
  • GMM mixture order M = not specified
    Number of Gaussian components in the velocity-space compression, Section III-D; chosen by the user and not reported.
  • velocity-space binning resolution = not specified
    3D histogram binning used before GMM fitting; resolution determines compression accuracy and is not reported in Section III-D.
  • GMM compression configuration parameters = tunable
    The paper notes compression ratio, accuracy, and cost can be tuned (Section III-D), so the reported 1000x ratio depends on untracked settings.
assumptions (4)
  • domain assumption Implicit moment-based PIC remains accurate for time steps and grid spacings 10x larger than explicit stability limits (from refs [4], [34]).
    The paper relies on this to claim a 10^4 reduction in resolution requirements, but does not re-derive or validate it in this work.
  • domain assumption FLOPs counted by rocprof on one node, multiplied by node count and measured parallel efficiency, give a valid sustained performance projection.
    Table IV and Section IV use this extrapolation for the 22.4 PFLOP/s headline number.
  • domain assumption Gaussian mixture models faithfully represent velocity distribution functions of electrons and ions.
    Invoked in Section III-D to justify the compression and downstream analytics.
  • domain assumption The 200 d_i by 100 d_i by 100 d_i simulation with one billion particles is a physically representative global magnetosphere.
    Figure 9 is offered as evidence of the new modeling regime, but no comparison to observations or known theory is given.

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Cite this review

Pith. "Pith review of Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics." pith.science (2026). https://pith.science/paper/ZRWT2WEW

@misc{pith2026250720719,
  author       = {Pith},
  title        = {Pith review of: Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZRWT2WEW}},
  note         = {Machine review of arXiv:2507.20719}
}
read the original abstract

Our fully kinetic, implicit Particle-in-Cell (PIC) simulations of global magnetospheres on up to 32,768 of El Capitan's AMD Instinct MI300A Accelerated Processing Units (APUs) represent an unprecedented computational capability that addresses a fundamental challenge in space physics: resolving the multi-scale coupling between microscopic (electron-scale) and macroscopic (global-scale) dynamics in planetary magnetospheres. The implicit scheme of iPIC3D supports time steps and grid spacing that are up to 10 times larger than those of explicit methods, without sacrificing physical accuracy. This enables the simulation of magnetospheres while preserving fine-scale electron physics, which is critical for key processes such as magnetic reconnection and plasma turbulence. Our algorithmic and technological innovations include GPU-optimized kernels, particle control, and physics-aware data compression using Gaussian Mixture Models. With simulation domains spanning 100-1,000 ion skin depths, we reach the global scale of small-to-medium planetary magnetospheres, such as those of Mercury and Ganymede, which supports fully kinetic treatment of global-scale dynamics in systems previously out of reach for fully kinetic PIC codes.

Figures

Figures reproduced from arXiv: 2507.20719 by the authors.

Figure 1
Figure 1. Different spatial and temporal scales in the Earth’s magnetosphere. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Profiling of a single iPIC3D time step on an Nvidia Grace Hopper Superchip using the Nvidia Nsight tool. Blue labels indicate GPU kernels, while [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Left: Execution time of a 500 cycles magnetic reconnection [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Compression ratios achieved by different compression algorithms. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 6
Figure 6. Figure 6: Scaling performance on El Capitan. Left Panel (Strong Scaling): [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Parallel efficiency for strong and weak scaling on LUMI-G, [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: I/O performance and checkpointing overhead on El Capitan. The [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Volume rendering of the electron density [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Observational Analysis of Multi-thermal Counter-streaming Flows in a Forming Filament and Their Relationship with Local Heating at Filament Footpoints

    astro-ph.SR 2025-08 unverdicted novelty 5.0 of 10

    An abstract on solar filament flows and footpoint heating is attached to a manuscript body about AMD MI300A unified physical memory, leaving the solar analysis completely unsupported.

Reference graph

Works this paper leans on

53 extracted references · 51 canonical work pages · cited by 1 Pith paper

  1. [1]

    The configuration of the magnetosphere,

    C. T. Russell, “The configuration of the magnetosphere,” in Critical problems of magnetospheric physics , p. 1, 1972

    work page 1972

  2. [2]

    Motions in the magnetosphere of the Earth,

    T. Gold, “Motions in the magnetosphere of the Earth,” Journal of Geophysical Research, vol. 64, no. 9, pp. 1219–1224, 1959

    work page 1959

  3. [3]

    What is space weather?,

    D. Baker, “What is space weather?,” Advances in Space Research , vol. 22, no. 1, pp. 7–16, 1998

    work page 1998

  4. [4]

    Space weather prediction and exascale comput- ing,

    G. Lapenta and et al., “Space weather prediction and exascale comput- ing,” Computing in Science & Engineering , vol. 15, no. 5, pp. 68–76, 2012

    work page 2012

  5. [5]

    Progress towards physics-based space weather forecasting with exascale computing,

    M. E. Innocenti and et al., “Progress towards physics-based space weather forecasting with exascale computing,” Advances in Engineering Software, vol. 111, pp. 3–17, 2017

    work page 2017

  6. [6]

    Kinetic approach to microscopic- macroscopic coupling in space and laboratory plasmas,

    G. Lapenta, J. Brackbill, and P. Ricci, “Kinetic approach to microscopic- macroscopic coupling in space and laboratory plasmas,” Physics of plasmas, vol. 13, no. 5, 2006

    work page 2006

  7. [7]

    Micro-macro coupling in plasma self- organization processes during island coalescence,

    W. Wan and G. Lapenta, “Micro-macro coupling in plasma self- organization processes during island coalescence,” Physical review let- ters, vol. 100, no. 3, p. 035004, 2008

    work page 2008

  8. [8]

    SWIFF: Space weather integrated forecasting framework,

    G. Lapenta and et al., “SWIFF: Space weather integrated forecasting framework,” Journal of Space Weather and Space Climate , vol. 3, p. A05, 2013

    work page 2013

Show all 53 references
  1. [9]

    Magnetic reconnection in plasmas,

    D. Biskamp, “Magnetic reconnection in plasmas,” Astrophysics and Space Science, vol. 242, pp. 165–207, 1996

    1996

  2. [10]

    Secondary reconnection sites in reconnection- generated flux ropes and reconnection fronts,

    G. Lapenta and et al., “Secondary reconnection sites in reconnection- generated flux ropes and reconnection fronts,” Nature Physics, vol. 11, no. 8, pp. 690–695, 2015

    2015

  3. [11]

    The Earth’s magnetosphere: A systems science overview and assessment,

    J. E. Borovsky and J. A. Valdivia, “The Earth’s magnetosphere: A systems science overview and assessment,” Surveys in geophysics , vol. 39, no. 5, pp. 817–859, 2018

    2018

  4. [12]

    Hydromagnetic flow around the magnetosphere,

    J. R. Spreiter, A. L. Summers, and A. Y . Alksne, “Hydromagnetic flow around the magnetosphere,” Planetary and Space Science, vol. 14, no. 3, pp. 223–253, 1966

    1966

  5. [13]

    Energetics of kinetic reconnection in a three-dimensional null-point cluster,

    V . Olshevsky, G. Lapenta, and S. Markidis, “Energetics of kinetic reconnection in a three-dimensional null-point cluster,” Physical review letters, vol. 111, no. 4, p. 045002, 2013

    2013

  6. [14]

    Magnetic null points in kinetic simulations of space plasmas,

    V . Olshevsky, J. Deca, A. Divin, I. B. Peng, S. Markidis, M. E. Innocenti, E. Cazzola, and G. Lapenta, “Magnetic null points in kinetic simulations of space plasmas,” The Astrophysical Journal , vol. 819, no. 1, p. 52, 2016

    2016

  7. [15]

    Space Weather Modeling Framework: A new tool for the space science community,

    G. T ´oth and et al., “Space Weather Modeling Framework: A new tool for the space science community,” Journal of Geophysical Research: Space Physics, vol. 110, no. A12, 2005

    2005

  8. [16]

    What sustained multi-disciplinary research can achieve: The Space Weather Modeling Framework,

    T. I. Gombosi and et al., “What sustained multi-disciplinary research can achieve: The Space Weather Modeling Framework,” Journal of Space Weather and Space Climate , vol. 11, p. 42, 2021

    2021

  9. [17]

    TRISTAN code and its applications,

    K. Nishikawa, “TRISTAN code and its applications,” Proceedings of ISSS-6, pp. 1–4, 2001

    2001

  10. [18]

    Solar wind- magnetosphere interaction as simulated by a 3-D EM particle code,

    O. Buneman, T. Neubert, and K.-I. Nishikawa, “Solar wind- magnetosphere interaction as simulated by a 3-D EM particle code,” IEEE transactions on plasma science, vol. 20, no. 6, pp. 810–816, 1992

    1992

  11. [19]

    Sensitivity of the Earth’s magnetosphere to solar wind activity: Three-dimensional macroparticle model,

    S. Baraka and L. Ben-Jaffel, “Sensitivity of the Earth’s magnetosphere to solar wind activity: Three-dimensional macroparticle model,” Journal of Geophysical Research: Space Physics , vol. 112, no. A6, 2007

    2007

  12. [20]

    TRISTAN-MP: TRIdimensional STANford - Massively Parallel code

    A. Spitkovsky and et al., “TRISTAN-MP: TRIdimensional STANford - Massively Parallel code.” Astrophysics Source Code Library, record ascl:1908.008, Aug. 2019

    1908

  13. [21]

    Vlasiator: First global hybrid-Vlasov simula- tions of Earth’s foreshock and magnetosheath,

    S. V on Alfthan and et al., “Vlasiator: First global hybrid-Vlasov simula- tions of Earth’s foreshock and magnetosheath,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 120, pp. 24–35, 2014

    2014

  14. [22]

    Hybrid-VPIC: An open-source kinetic/fluid hybrid Particle-in-Cell code,

    A. Le and et al., “Hybrid-VPIC: An open-source kinetic/fluid hybrid Particle-in-Cell code,” Physics of Plasmas , vol. 30, no. 6, 2023

    2023

  15. [23]

    AMITIS: A 3D GPU-based hybrid-PIC model for space and plasma physics,

    S. Fatemi and et al., “AMITIS: A 3D GPU-based hybrid-PIC model for space and plasma physics,” in Journal of physics: Conference series , vol. 837, p. 012017, IOP Publishing, 2017

    2017

  16. [24]

    AIKEF: Adaptive hybrid model for space plasma simulations,

    J. M ¨uller and et al., “AIKEF: Adaptive hybrid model for space plasma simulations,” Computer Physics Communications , vol. 182, no. 4, pp. 946–966, 2011

    2011

  17. [25]

    Two-way coupling of a global Hall mag- netohydrodynamics model with a local implicit Particle-in-Cell model,

    L. K. Daldorff and et al., “Two-way coupling of a global Hall mag- netohydrodynamics model with a local implicit Particle-in-Cell model,” Journal of Computational Physics , vol. 268, pp. 236–254, 2014

    2014

  18. [26]

    Magnetohydrodynamic with adaptively embed- ded particle-in-cell model: MHD-AEPIC,

    Y . Shou and et al., “Magnetohydrodynamic with adaptively embed- ded particle-in-cell model: MHD-AEPIC,” Journal of Computational Physics, vol. 446, p. 110656, 2021

    2021

  19. [27]

    Extended magnetohydrodynamics with embedded Particle-in-Cell simulation of Ganymede’s magnetosphere,

    G. T ´oth and et al., “Extended magnetohydrodynamics with embedded Particle-in-Cell simulation of Ganymede’s magnetosphere,” Journal of Geophysical Research: Space Physics , vol. 121, no. 2, pp. 1273–1293, 2016

    2016

  20. [28]

    Embedded kinetic simulation of Ganymede’s magnetosphere: Improvements and inferences,

    H. Zhou and et al., “Embedded kinetic simulation of Ganymede’s magnetosphere: Improvements and inferences,” Journal of Geophysical Research: Space Physics , vol. 124, no. 7, pp. 5441–5460, 2019

    2019

  21. [29]

    Studying dawn-dusk asymmetries of Mercury’s magnetotail using MHD-EPIC simulations,

    Y . Chen and et al., “Studying dawn-dusk asymmetries of Mercury’s magnetotail using MHD-EPIC simulations,” Journal of Geophysical Research: Space Physics , vol. 124, no. 11, pp. 8954–8973, 2019

    2019

  22. [30]

    Magnetohydrodynamic with embedded Particle-in- Cell simulation of the Geospace Environment Modeling dayside kinetic processes challenge event,

    Y . Chen and et al., “Magnetohydrodynamic with embedded Particle-in- Cell simulation of the Geospace Environment Modeling dayside kinetic processes challenge event,” Earth and Space Science , vol. 7, no. 11, 2020

    2020

  23. [31]

    The fluid-kinetic Particle-in-Cell method for plasma simulations,

    S. Markidis and et al., “The fluid-kinetic Particle-in-Cell method for plasma simulations,” Journal of Computational Physics , vol. 271, pp. 415–429, 2014

    2014

  24. [32]

    C. K. Birdsall and A. B. Langdon, Plasma physics via computer simulation. CRC press, 2018

    2018

  25. [33]

    Particle simulation of plasmas,

    J. M. Dawson, “Particle simulation of plasmas,” Reviews of modern physics, vol. 55, no. 2, p. 403, 1983

    1983

  26. [34]

    Multi-scale simulations of plasma with iPIC3D,

    S. Markidis, G. Lapenta, and Rizwan-uddin, “Multi-scale simulations of plasma with iPIC3D,” Mathematics and Computers in Simulation , vol. 80, no. 7, pp. 1509–1519, 2010

    2010

  27. [35]

    Energetic particles in magnetotail reconnection,

    I. B. Peng and et al., “Energetic particles in magnetotail reconnection,” Journal of Plasma Physics , vol. 81, no. 2, p. 325810202, 2015

    2015

  28. [36]

    A flexible inner-outer preconditioned GMRES algorithm,

    Y . Saad, “A flexible inner-outer preconditioned GMRES algorithm,” SIAM Journal on Scientific Computing , vol. 14, no. 2, pp. 461–469, 1993

    1993

  29. [37]

    An implicit method for electromag- netic plasma simulation in two dimensions,

    J. Brackbill and D. Forslund, “An implicit method for electromag- netic plasma simulation in two dimensions,” Journal of Computational Physics, vol. 46, no. 2, pp. 271–308, 1982

    1982

  30. [38]

    Simulation of low-frequency, electro- magnetic phenomena in plasmas,

    J. Brackbill and D. Forslund, “Simulation of low-frequency, electro- magnetic phenomena in plasmas,” in Multiple time scales, pp. 271–310, Elsevier, 1985

    1985

  31. [39]

    The formation of a magnetosphere with im- plicit Particle-in-Cell simulations,

    I. B. Peng and et al., “The formation of a magnetosphere with im- plicit Particle-in-Cell simulations,” Procedia Computer Science, vol. 51, pp. 1178–1187, 2015

    2015

  32. [40]

    Dynamic and selective control of the number of particles in kinetic plasma simulations,

    G. Lapenta and J. U. Brackbill, “Dynamic and selective control of the number of particles in kinetic plasma simulations,” Journal of Computational Physics, vol. 115, no. 1, pp. 213–227, 1994

    1994

  33. [41]

    Moment preserving constrained resampling with applications to Particle-in-Cell methods,

    D. Faghihi and et al., “Moment preserving constrained resampling with applications to Particle-in-Cell methods,” Journal of Computational Physics, vol. 409, p. 109317, 2020

    2020

  34. [42]

    An unsupervised machine-learning checkpoint- restart algorithm using Gaussian mixtures for Particle-in-Cell simula- tions,

    G. Chen and et al., “An unsupervised machine-learning checkpoint- restart algorithm using Gaussian mixtures for Particle-in-Cell simula- tions,” Journal of Computational Physics , vol. 436, p. 110185, 2021

    2021

  35. [43]

    Physics-aware compression of plasma distri- bution functions with GPU-accelerated Gaussian Mixture Models,

    H. Andong and et al., “Physics-aware compression of plasma distri- bution functions with GPU-accelerated Gaussian Mixture Models,” in International Conference on Computational Science , Springer’s LNCS Series, 2025

    2025

  36. [44]

    McLachlan and T

    G. McLachlan and T. Krishnan, The EM algorithm and extensions . Wiley, 1997

    1997

  37. [45]

    ADIOS 2: The adaptable input output system. A framework for high-performance data management,

    W. F. Godoy and et al., “ADIOS 2: The adaptable input output system. A framework for high-performance data management,” SoftwareX, vol. 12, p. 100561, 2020

    2020

  38. [46]

    Data reduction techniques for simulation, visualization and data analysis,

    S. Li and et al., “Data reduction techniques for simulation, visualization and data analysis,” in Computer graphics forum , vol. 37, pp. 422–447, Wiley Online Library, 2018

    2018

  39. [47]

    A survey on error-bounded lossy compression for scientific datasets,

    S. Di and et al., “A survey on error-bounded lossy compression for scientific datasets,” arXiv preprint arXiv:2404.02840 , 2024

    2024 arXiv

  40. [48]

    The artificial scientist–in-transit machine learning of plasma simulations,

    J. Kelling and et al., “The artificial scientist–in-transit machine learning of plasma simulations,” arXiv preprint arXiv:2501.03383 , 2025

    2025 arXiv

  41. [49]

    ruptures: change point detection in Python,

    C. Truong, L. Oudre, and N. Vayatis, “ruptures: change point detection in Python,” arXiv preprint arXiv:1801.00826 , 2018

    2018 arXiv

  42. [50]

    Geospace Environmental Modeling (GEM) mag- netic reconnection challenge,

    J. Birn and et al., “Geospace Environmental Modeling (GEM) mag- netic reconnection challenge,” Journal of Geophysical Research: Space Physics, vol. 106, no. A3, pp. 3715–3719, 2001

    2001

  43. [51]

    Pushing the frontier in the design of laser-based electron accelerators with groundbreaking mesh-refined Particle-in-Cell simulations on exascale-class supercomputers,

    L. Fedeli and et al., “Pushing the frontier in the design of laser-based electron accelerators with groundbreaking mesh-refined Particle-in-Cell simulations on exascale-class supercomputers,” in SC22: international conference for high performance computing, networking, storage...

    2022

  44. [52]

    Kinetic simulations of plasmoid chain dynamics,

    S. Markidis, P. Henri, G. Lapenta, A. Divin, M. Goldman, D. Newman, and E. Laure, “Kinetic simulations of plasmoid chain dynamics,”Physics of Plasmas, vol. 20, no. 8, 2013

    2013

  45. [53]

    Collisionless magnetic reconnection in a plas- moid chain,

    S. Markidis, P. Henri, G. Lapenta, A. Divin, M. V . Goldman, D. New- man, and S. Eriksson, “Collisionless magnetic reconnection in a plas- moid chain,” Nonlinear processes in geophysics, vol. 19, no. 1, pp. 145– 153, 2012

    2012

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.