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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count% of corpus
0111 · Jul 202119922001200920172026
4 results for “gyrokinetics”
GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulationsphysics.plasm-ph

GyroSwin models plasma turbulence with neural nets, reducing costs and capturing neglected nonlinearities.

problem Understanding plasma turbulence in fusion reactors, which impairs confinement and limits reactor design.
method Introduces GyroSwin, a scalable 5D neural surrogate that approximates 5D nonlinear gyrokinetic simulations.
result GyroSwin outperforms reduced models in heat flux prediction and captures turbulent energy cascade.
plasma turbulencegyrokineticsneural surrogatesfusion energyturbulent transport
2025-10-08abs ↗pdf ↗
5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma Turbulencephysics.plasm-ph

Neural surrogates speed up 5D gyrokinetic simulations of plasma turbulence.

problem Expensive numerical simulations of plasma turbulence hinder fusion reactor design.
method Trained a hierarchical vision transformer in 5D to predict plasma quantities faster.
result Neural surrogates predict plasma quantities two orders of magnitude faster than numerical codes.
neural surrogatesplasma turbulencegyrokinetic simulationsnuclear fusionfusion power
2025-02-11abs ↗pdf ↗
Turbulent field fluctuations in gyrokinetic and fluid plasmasphysics.plasm-ph

Deep learning compares turbulence models in plasma physics.

problem Predicting edge plasma turbulence in magnetic fusion reactors.
method Physics-informed deep learning framework for comparing two-fluid and gyrokinetic models.
result Good overall agreement between two-fluid theory and gyrokinetic models in turbulent field fluctuations.
plasma turbulencemagnetic confinement fusiondeep learninggyrokineticstwo-fluid theory
2021-07-20abs ↗pdf ↗
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experimentphysics.plasm-ph

Physics-informed ML models improve turbulence understanding in fusion plasmas.

problem Improving turbulence modeling in fusion plasma devices.
method Physics-informed deep learning framework constrained by PDEs.
result Direct quantitative comparisons of turbulent fields between theory and gyrokinetic models.
fusion plasmaturbulencemachine learningtwo-fluid theorygyrokinetics
2022-05-16abs ↗pdf ↗