General & Applied Physics · Physics & Astronomy
Plasma Physics
The newest papers in Plasma Physics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
37 papers, from 10 September 2026 to 5 November 2026: 37 arXiv preprints.
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Newest papers, page 2
Quantum Circuits for Variable-Coefficient Vlasov Dynamics via Schrödingerisation
High-dimensional Vlasov equations present promising use cases for quantum simulation, but their transport coefficients must be implemented coherently in order to preserve a compact quantum…The impact of nonlinear Landau damping on cosmic ray streaming and the role of external turbulence
Cosmic rays (CRs) streaming faster than the Alfvén speed generate Alfvén waves that scatter them.Toward a Multimodal Foundation Model for Plasma State Reconstruction
Plasma experiments observe coupled physical states through sparse, heterogeneous, and incomplete diagnostics.Reconstruction of Multiscale Plasma Dynamics Across Operating Regimes
Reconstructing spatially resolved plasma dynamics from few sensors is essential for diagnostics, reduced-order modelling and control, yet remains difficult because the sparse measurements…Quantum approaches for particle-in-cell codes
Solving the high-dimensional, nonlinear partial differential equations that govern the rich physics of plasmas remains a major computational challenge.Parity-Time Symmetry of Plasma Waves
The susceptibility tensor of a homogeneous collisionless magnetized plasma possesses parity-time (PT) symmetry.IGNITE Tokamak World Model Architecture
We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data at the DIII-D…PHASE: Multi-Regime Modeling of Incompressible Magnetohydrodynamics
Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics is computationally expensive.A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling
Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations.A packet-level digital hardware twin for commissioning megahertz diagnostic edge AI and plasma control system integration in tokamaks
High-bandwidth plasma diagnostics increasingly provide inputs to machine learning and signal-processing algorithms intended for real-time tokamak control, but the complete path from diagnostic…From Processing to Functionality: Engineering Accessible Material States in Cu-Embedded SiO$_x$ Memristive Devices
Resistive switching in oxide-based devices is widely governed by stochastic defect processes, yet a predictive link between fabrication conditions and functional behavior remains elusive.Saturation Mechanism of Cosmic Ray Streaming Instabilities with a Shell Distribution
Cosmic rays (CRs) drive galactic winds and regulate galaxy growth while contributing to heating the central cooling plasma in dense galaxy clusters, making the nonlinear saturation of the…Probabilistic and Geometry Aware Neural Surrogate of Scrape Off Layer Plasma Simulations
Fast surrogates for tokamak boundary-plasma simulation are typically deterministic regressors mapping a global operating point to a flattened vector of cell values.Electronic-Entropy-Driven Phase Transitions in Compressed Iron Oxides
Electronic entropy is usually treated as a secondary correction to structural stability, but under strong electronic excitation it can become a primary thermodynamic driving force.Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms
Reliable disruption prediction is essential for the safe operation of future tokamaks.Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks
Real time plasma state estimation for control of Tokamak devices are challenging due to the stringent latency requirements of the plasma control system (PCS).Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and…
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