Adaptive AI-Laser Plasma Control for Linear Fusion Reactors Toward Predictive Turbulence Stabilization Using Femtosecond Diagnostics and Real-Time Digital Twin Architectures

AbstractMagnetic mirror and linear fusion systems historically suffer from turbulence-driven con-finement degradation, including firehose, drift-cyclotron loss-cone (DCLC), mirror, and ion-cyclotron instabilities. Classical stabilization strategies relied primarily on passive magneticgeometry optimization and static confinement architectures.This work proposes a radically different paradigm: active metastability through ultrafastobservation and predictive control.We introduce a conceptual architecture combining:• femtosecond laser plasma diagnostics,• AI-driven turbulence prediction,• reduced-order generative plasma simulation,• adaptive magnetic feedback control,• real-time plasma digital twins.Instead of attempting to eliminate turbulence entirely, the system aims to maintain theplasma inside dynamically controlled metastable attractors.The proposed framework couples kinetic plasma theory, Lyapunov adaptive control,machine-learning surrogate models, and ultrafast spectroscopy into a unified closed-loopstabilization architecture potentially applicable to:• linear magnetic mirrors,• tandem mirrors,• plasma propulsion systems,• advanced open-ended fusion devices.We discuss physical limitations, latency constraints, instability forecasting, and futureexperimental pathways.