3 papers
physics.plasm-ph2026
TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion
Yadi Cao, Futian Zhang, Wesley Liu +7
The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations r…
quant-ph2026
Physics-Informed Hybrid Quantum-Classical Dispatching for Large-Scale Renewable Power Systems:A Noise-Resilient Framework
Fu Zhang, Yuming Zhao
The integration of high-penetration renewable energy introduces significant stochasticity and non-convexity into power system dispatching, challenging the computational limits of c…
quant-ph2026
Hybrid Quantum-Classical Dispatching for High-Renewable Power Systems:A Noise-Resilient Variational Approach with Real-World Validation
Fu Zhang, Yuming Zhao
This study introduces a hybrid quantum-classical dispatching framework designed for power systems with high renewable penetration. The proposed method integrates a variational quan…