A5102378959- Solar and Space Plasma Dynamics
- Fluid Dynamics and Turbulent Flows
- Electric Motor Design and Analysis
- Ecosystem dynamics and resilience
- Plasma and Flow Control in Aerodynamics
- Complex Systems and Time Series Analysis
- Neural Networks and Applications
- Stellar, planetary, and galactic studies
- Complex Network Analysis Techniques
- Astro and Planetary Science
- Model Reduction and Neural Networks
- Magnetic confinement fusion research
Yantai University
2024
China Academy of Launch Vehicle Technology
2020
Physics-informed neural networks (PINNs) have been employed as a new type of solver partial differential equations (PDEs). However, PINNs suffer from two limitations that impede their further development. First, exhibit weak physical constraints may result in unsatisfactory results for complex problems. Second, the operation using automatic differentiation (AD) loss function contaminate backpropagated gradients hindering convergence networks. To address these issues and improve ability...
Serial correlations within temperature time series serve as indicators of the temporal consistency climate events. This study delves into serial embedded in global surface air (SAT) data. Initially, we preprocess SAT to eradicate seasonal patterns and linear trends, resulting anomaly series, which encapsulates inherent variability Earth's system. Employing diverse statistical techniques, identify three distinct types correlations: short-term, long-term, nonlinear. To short-term correlations,...
The magnetohydrodynamic (MHD) control has great potential in the applications of hypersonic vehicles. Typically, MHD flowfield these is low magnetic Reynolds (Rem) compressible turbulent flow, which different from that without field and within high Rem range. This paper investigated isotropic turbulence Taylor-scale numbers interaction parameters via Direct Numerical Simulation (DNS), influence on researched. Results indicate normal stress decreases direction perpendicular to field....