Compréhension, prédiction et contrôle du transport turbulent des particules énergétiques dans les plasmas de fusion nucléaire par des simulations de trajectoires et des techniques d’intelligence artificielle (Thèse 2022 - 2025)
Activités
Fusion par confinement magnétique
Particules énergétiques
Simulation de trajectoires : codes gyro-cinétiques, trajectoires full-orbit
Intelligence artificielle
Publications scientifiques au M2P2
2025
B Clavier, D Zarzoso, D Del-Castillo-Negrete, E Frénod. A Generative Artificial Intelligence framework for long-time plasma turbulence simulations. Physics of Plasmas, In press, ⟨10.1063/5.0255386⟩. ⟨hal-05085168⟩ Plus de détails...
Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatio-temporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa-Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatio-temporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo-Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and in the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyper-parameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of AI-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.
B Clavier, D Zarzoso, D Del-Castillo-Negrete, E Frénod. A Generative Artificial Intelligence framework for long-time plasma turbulence simulations. Physics of Plasmas, In press, ⟨10.1063/5.0255386⟩. ⟨hal-05085168⟩
B. Clavier, D. Zarzoso, D. Del-Castillo-Negrete, E. Frénod. Generative-machine-learning surrogate model of plasma turbulence. Physical Review E , 2025, 111 (1), pp.L013202. ⟨10.1103/PhysRevE.111.L013202⟩. ⟨hal-04966199⟩ Plus de détails...
Generative artificial intelligence methods are employed for the first time to construct a surrogate model for plasma turbulence that enables long-time transport simulations. The proposed GAIT (Generative Artificial Intelligence Turbulence) model is based on the coupling of a convolutional variational autoencoder that encodes precomputed turbulence data into a reduced latent space, and a recurrent neural network and decoder that generate new turbulence states 400 times faster than the direct numerical integration. The model is applied to the Hasegawa-Wakatani (HW) plasma turbulence model, which is closely related to the quasigeostrophic model used in geophysical fluid dynamics. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, and the flow topology characterized by the Okubo-Weiss decomposition. The GAIT model also reproduces Lagrangian transport including the probability distribution function of particle displacements and the effective turbulent diffusivity.
B. Clavier, D. Zarzoso, D. Del-Castillo-Negrete, E. Frénod. Generative-machine-learning surrogate model of plasma turbulence. Physical Review E , 2025, 111 (1), pp.L013202. ⟨10.1103/PhysRevE.111.L013202⟩. ⟨hal-04966199⟩
B. Clavier, D. Zarzoso, Diego Del-Castillo-Negrete, E. Frénod. A generative machine learning surrogate model of plasma turbulence. Physical Review E , 2025, 111 (1), pp.L013202. ⟨10.1103/PhysRevE.111.L013202⟩. ⟨hal-04600564⟩ Plus de détails...
State-of-the-art techniques in generative artificial intelligence are employed for the first time to construct a surrogate model for plasma turbulence that enables long time transport simulations. The proposed GAIT (Generative Artificial Intelligence Turbulence) model is based on the coupling of a convolutional variational auto-encoder, that encodes precomputed turbulence data into a reduce latent space, and a deep neural network and decoder that generate new turbulence states 400 times faster than the direct numerical integration. The model is applied to the Hasegawa-Wakatani (HW) plasma turbulence model, that is closely related to the quasigeostrophic model used in geophysical fluid dynamics. Very good agreement is found between the GAIT and the HW models in the spatio-temporal Fourier and Proper Orthogonal Decomposition spectra as well as in the flow topology characterized by the Okubo-Weiss decomposition. Agreement is also found in the probability distribution function of particle displacements and the effective turbulent diffusivity.
B. Clavier, D. Zarzoso, Diego Del-Castillo-Negrete, E. Frénod. A generative machine learning surrogate model of plasma turbulence. Physical Review E , 2025, 111 (1), pp.L013202. ⟨10.1103/PhysRevE.111.L013202⟩. ⟨hal-04600564⟩
