🔬 Category

physics.plasm-ph

534 papers

Plasma physics explores the behavior of the fourth state of matter, a superheated soup of charged particles that makes up most of the visible universe. From the fusion power we hope to harness on Earth to the glowing auroras and distant stars above, this field investigates how these energetic gases interact with magnetic fields and light. It is a dynamic area where extreme conditions reveal fundamental laws of nature in ways solid matter never can.

At Gist.Science, we bridge the gap between these complex discoveries and curious minds by processing every new preprint from arXiv in this category. We transform dense, technical research into clear, plain-language explanations alongside detailed summaries, ensuring that breakthroughs in plasma dynamics and fusion energy are accessible to everyone. Below are the latest papers in plasma physics, curated and simplified for your reading.

Attosecond Nonlinear Quantum Electrodynamics in Laser-Driven Plasmas via Two-Photon Synchrotron Emission

This paper proposes that ultrafast strong-field laser-plasma interactions can serve as a self-contained framework for relativistic nonlinear quantum electrodynamics by generating attosecond bursts of two-photon synchrotron emission from laser-accelerated electrons, thereby providing a distinct pathway to isolate and study quantum phenomena without requiring external relativistic particle beams.

Vedin Dewan, Aleksei M. Zheltikov, Julia M. Mikhailova2026-04-23🔬 physics

Generation and Enhancement of Persistent Nanoscale Magnetization in All-Dielectric Metasurfaces by Optically Injected and Localized Free Carriers

This paper demonstrates that rapidly changing all-dielectric metasurfaces via optically injected free carriers can act as temporal interfaces to generate persistent, nanoscale quasistatic magnetic fields supported by residual circulating currents, alongside frequency-shifted and time-refracted metasurface-guided waves.

Shivaksh Rawat, Samyobrata Mukherjee, Gennady Shvets2026-04-22🔬 physics.app-ph

Deep-Learning based surrogate models for plasma exhaust simulations -- SOLPS-NN

This paper introduces SOLPS-NN, a deep-learning surrogate model trained on extensive SOLPS-ITER simulations that utilizes simple fully connected neural networks to efficiently and accurately predict plasma exhaust conditions and detachment access, demonstrating that independent models for specific observables yield higher accuracy and that transfer learning offers no significant advantage over training from scratch.

Stefan Dasbach, Sebastijan Brezinsek, Yunfeng Liang, Dirk Reiser, Sven Wiesen2026-04-22🔬 physics

Ion wake-mediated dust interactions under PK-4 conditions: a generalized and compact potential formulation

This paper presents a robust, generalized potential model for ion wake-mediated dust interactions under PK-4 conditions, which uses a minimal set of coefficients derived from molecular dynamics simulations to accurately capture potential distributions across various dust arrangements beyond traditional string-like configurations.

Diana Jimenez Marti, Benny Rodriguez Saenz, Peter Hartmann, Evdokiya Kostadinova, Truell Hyde, Lorin Swint Matthews2026-04-22🔬 physics

Periodic Korteweg-de Vries soliton potentials generate quasisymmetric magnetic fields

This paper establishes a deep connection between quasisymmetric magnetic fields in stellarators and soliton theory by demonstrating that periodic Korteweg-de Vries soliton potentials generate these fields, a relationship validated through non-perturbative mathematical analysis and machine learning that reveals hidden lower-dimensional symmetries and potential divertor configurations.

W. Sengupta, N. Nikulsin, S. Buller, R. Madan, E. J. Paul, R. Nies, A. A. Kaptanoglu, S. R. Hudson, A. Bhattacharjee2026-04-21🔬 physics

TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion

This paper introduces TGLF-WINN, a data-efficient deep learning surrogate for turbulent transport modeling in fusion that combines physics-guided feature engineering, wavenumber-resolved regularization, and Bayesian Active Learning to achieve high accuracy with significantly reduced training data requirements while enabling a 45x speedup over the traditional TGLF model.

Yadi Cao, Futian Zhang, Wesley Liu, Tom Neiser, Orso Meneghini, Lawson Fuller, Sterling Smith, Raffi Nazikian, Brian Sammuli, Rose Yu2026-04-21🔬 physics

Learning time-dependent and integro-differential collision operators from plasma phase space data using differentiable simulators

This paper presents a methodology that leverages differentiable kinetic simulators and plasma phase space data to learn time-dependent and integro-differential collision operators, demonstrating their ability to accurately reproduce complex non-equilibrium plasma dynamics more effectively than traditional particle track statistics.

Diogo D. Carvalho, Luis O. Silva, E. Paulo Alves2026-04-21🔬 physics

Thermal Effects on Buneman Instability: A Vlasov-Poisson Study

This Vlasov-Poisson study investigates the thermal effects on Buneman instability, revealing that while the maximum growth rate retains its (m/M)1/3(m/M)^{1/3} dependence, it remains largely independent of the temperature ratio, and that ion density inhomogeneity self-consistently governs the efficiency of electron beam energy transfer to bulk plasma heating.

Chingangbam Amudon, Sanjeev Kumar Pandey, Rajaraman Ganesh2026-04-21🔬 physics