5 papers
FusionMMT: A Unified Multimodal and Multitask Learning Framework for Nuclear Fusion
Qiang Chen, Xiao Wang, Qingquan Yang +5
With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the leading approaches to magnetic…
Physics-Informed Neural Networks for Complex Eigenfrequency Identification and Mode Structure Reconstruction of the Ground-State ITG Branch
Dengdi Sun, Bingbing Zhang, Xiao Wang +5
Physics-informed neural networks (PINNs) combine sparse observations with physical equations, providing an important approach for modeling complex plasma processes and inferring un…
Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
Zikang Yan, Xiao Wang, Qingquan Yang +6
Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices. However, conventio…
Axion-like Particle Search with a Light-Shining-Through-Walls Setup at a - Collider
Zi-Yao Yan, Jie Feng
In this work, we have explored a practical extension of the conventional light-shining-through-walls technique by making direct use of the high-intensity -ray beam available at…
Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network
Xiao Wang, Zikang Yan, Hao Si +5
Estimating heat flux in the nuclear fusion device EAST is a critically important task. Traditional scientific computing methods typically model this process using the Finite Elemen…