A5107891270- Model Reduction and Neural Networks
- Numerical methods for differential equations
- Advanced Numerical Methods in Computational Mathematics
- Power System Optimization and Stability
- Organ Transplantation Techniques and Outcomes
- Pancreatic function and diabetes
- Tensor decomposition and applications
- Liver physiology and pathology
- Electromagnetic Simulation and Numerical Methods
University of Twente
2023
Xi'an Jiaotong University
2021
Southwest University
2019
The liver has a high regenerative capacity. Upon two-thirds partial hepatectomy, the hepatocytes proliferate and contribute to regeneration. After severe injury, when proliferation of residual is blocked, biliary epithelial cells (BECs) lose their morphology express hepatoblast endoderm markers, dedifferentiate into bipotential progenitor (BP-PCs), then redifferentiate mature hepatocytes. Little known about mechanisms involved in formation BP-PCs after extreme injury. Using zebrafish injury...
In this paper, we for the first time explore model order reduction (MOR) of parametric systems based on tensor techniques and a parallel compression algorithm. For system characterising multidimensional parameter space nonlinear dependence, approximate matrices by functions parameters, whose first-order coefficients are third-order tensors. to effectively reduce computational cost storage burden, propose algorithm Tensor-SVD deal with tensors in functions. Then, obtain low-rank approximation...
This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical employ linear subspaces representing system states in a reduced-dimensional coordinate system. While these approximations respect nature systems, basis can suffer from slowly decaying Kolmogorov $N$-width, especially wave-type problems, which then requires large size. We propose different methods based on recently developed...