A machine learning model of 𝐷_𝑄𝐿 will be constructed using PINNs, which enables a more rapid and precise determination of 𝐷_𝑄𝐿. This process will incorporate physical constraints, specifically analytical solution of upper and lower bounds on phase velocity which determines wave propagation and damping region. The model’s construction employs comprehensive database from GENRAY-CQL3D simulations.
To construct the generalized DQL of the lower hybrid wave in a tokamak, we’ll employ PINNs, which involves the physics of wave propagation and damping in plasmas. The database regarding how wave propagates and is damped will be primarily constructed through the simulation of CQL3D, which utilizes bounce-averaged Fokker-Planck solvers on noncircular
magnetic flux surfaces. Additionally, GENRAY, a ray tracing code specifically designed for computing electromagnetic wave propagation and absorption, will be employed.
To construct the generalized DQL of the lower hybrid wave in a tokamak, we’ll employ PINNs, which involves the physics of wave propagation and damping in plasmas. The database regarding how wave propagates and is damped will be primarily constructed through the simulation of CQL3D, which utilizes bounce-averaged Fokker-Planck solvers on noncircular
magnetic flux surfaces. Additionally, GENRAY, a ray tracing code specifically designed for computing electromagnetic wave propagation and absorption, will be employed.
