3 citations · 3 across the 9 of their papers we have counts for
10 papers
Learning Reduced-Order Dynamics with Singularity via Latent-Augmented Neural Ordinary Differential Equations
Xiaorui Wang, Yu Zhou, Wenjie Mei +3
This paper addresses the issue of self-intersecting trajectories (in phase space) in industrial reduced-order modeling and proposes the Latent-Augmented Neural Ordinary Differentia…
Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
Shugen Song, Wenjie Mei, Chengyan Zhao
Model Predictive Path Integral (MPPI) control is a powerful sampling-based strategy for nonlinear autonomous systems. However, its performance is often bottlenecked by the fidelity…
A Posteriori Second-Order Guarantees for Bolza Problems via Collocation
Dongzhe Zheng, Wenjie Mei
Direct collocation for Bolza optimal control yields discrete Karush-Kuhn-Tucker (KKT) points, while practical solvers expose only discrete quantities such as primal-dual iterates,…
Multiscale Cross-Modal Mapping of Molecular, Pathologic, and Radiologic Phenotypes in Lipid-Deficient Clear Cell Renal CellCarcinoma
Ying Cui, Dongzhe Zheng, Ke Yu +8
Clear cell renal cell carcinoma (ccRCC) exhibits extensive intratumoral heterogeneity on multiple biological scales, contributing to variable clinical outcomes and limiting the eff…
Memory-Augmented Potential Field Theory: A Framework for Adaptive Control in Non-Convex Domains
Dongzhe Zheng, Wenjie Mei
Stochastic optimal control methods often struggle in complex non-convex landscapes, frequently becoming trapped in local optima due to their inability to learn from historical traj…
Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures
Dongzhe Zheng, Wenjie Mei
Learning unknown dynamics under environmental (or external) constraints is fundamental to many fields (e.g., modern robotics), particularly challenging when constraint information…
Learning and Current Prediction of PMSM Drive via Differential Neural Networks
Wenjie Mei, Xiaorui Wang, Yanrong Lu +2
Learning models for dynamical systems in continuous time is significant for understanding complex phenomena and making accurate predictions. This study presents a novel approach ut…
ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence
Wenjie Mei, Dongzhe Zheng, Shihua Li
Neural ODEs (NODEs) are continuous-time neural networks (NNs) that can process data without the limitation of time intervals. They have advantages in learning and understanding the…
ICODE: Modeling Dynamical Systems with Extrinsic Input Information
Zhaoyi Li, Wenjie Mei, Ke Yu +2
Learning models of dynamical systems with external inputs, which may be, for example, nonsmooth or piecewise, is crucial for studying complex phenomena and predicting future state…
Learning for System Identification of NDAE-modeled Power Systems
Wenjie Mei, Muhammad Nadeem, MirSaleh Bahavarnia +1
System identification through learning approaches is emerging as a promising strategy for understanding and simulating dynamical systems, which nevertheless faces considerable diff…