6 papers
Stereo-Planner: Learning Stereo-to-Trajectory Planning for Zero-Shot Sim-to-Real Quadrotor Navigation
Hongming Shen, Junjie Lu, Xun Chen +4
Existing navigation systems commonly rely on dedicated depth sensors or explicit depth reconstruction, while learning to generate trajectories directly from RGB images remains chal…
Machine learning prediction of plasma behavior from discharge configurations on WEST
Chenguang Wan, Feda Almuhisen, Philippe Moreau +10
Accurately predicting plasma behavior based on discharge configurations is essential for the safe and efficient operation of tokamak experiments. While physics-based integrated mod…
Reconstructing High-fidelity Plasma Turbulence with Data-driven Tuning of Diffusion in Low Resolution Grids
Kunpeng Li, Youngwoo Cho, Xavier Garbet +6
Developing physically consistent closure models is a longstanding challenge in simulating plasma turbulence, even in minimal systems such as the two-field Hasegawa-Wakatani (HW) mo…
Extracting a stochastic model for predator-prey dynamic of turbulence and zonal flows with limited data
J. C. Huang, Z. S. Qu, R. Varennes +6
Understanding the interaction between turbulence and zonal flows is critical for modeling turbulence transport in fusion plasmas, often described through predator-prey dynamics. Ho…
A high-fidelity surrogate model for the ion temperature gradient (ITG) instability using a small expensive simulation dataset
Chenguang Wan, Youngwoo Cho, Zhisong Qu +7
One of the main challenges in building high-fidelity surrogate models of tokamak turbulence is the substantial demand for high-quality data. Typically, producing high-quality data…
Turbulent relaxation patterns in SOL plasma
R Varennes, G Dif-Pradalier, P Ghendrih +5
Relaxations of localized over-density in a plane transverse to the magnetic field are numericallyinvestigated under the effect of drift-wave and interchange drives in SOL condition…