Lijian Jiang

Online monitoring of key components and switching frequency protection for flexible DC converter valves

Shaokun Deng, Biao Li, Lixuan Zhu, Dawei Chen et autres

Flexible High-Voltage Direct Current (HVDC) converter valves are the core equipment in flexible DC transmission systems, and the reliable operation of their key components (IGBTs and DC capacitors) is critical to system stability. However, existing monitoring systems mainly rely on Boolean state …

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Topological spectral gap, multiscale Weyl's law, and homogenization in high-contrast PDEs

Eric T. Chung, Lijian Jiang, Yajun Wang

This paper introduces a unified abstract variational and topological framework to characterize the spectral gap and eigenvalue distribution in high-contrast multiscale partial differential equations (PDEs). We rigorously prove that the exact location of the spectral gap is universally determined by the dimension …

Topological spectral gap, multiscale Weyl's law, and homogenization in high-contrast PDEs

Eric T. Chung, Lijian Jiang, Yajun Wang

This paper introduces a unified abstract variational and topological framework to characterize the spectral gap and eigenvalue distribution in high-contrast multiscale partial differential equations (PDEs). We rigorously prove that the exact location of the spectral gap is universally determined by the dimension …

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MsFEM-Inspired CNNs with Transfer Learning for Multiscale Model Reduction

Xuehan Zhang, Lijian Jiang, Eric T. Chung

Deep learning-based surrogate models have been extensively developed for efficiently approximating multiscale systems with random input fields. However, most existing approaches require retraining neural networks from scratch when source terms, boundary conditions, or differential operators change, resulting in significant computational costs and …

MsFEM-Inspired CNNs with Transfer Learning for Multiscale Model Reduction

Xuehan Zhang, Lijian Jiang, Eric T. Chung

Deep learning-based surrogate models have been extensively developed for efficiently approximating multiscale systems with random input fields. However, most existing approaches require retraining neural networks from scratch when source terms, boundary conditions, or differential operators change, resulting in significant computational costs and …

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Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems

Dengfei Zeng, Lijian Jiang, Shuyu Sun, Dunhui Xiao

A likelihood-free transport filtering method is proposed based on the couplings between state and observation variables. By exploiting a block-triangular structure in the transport map, the analysis step of filtering is reformulated as the minimization of the maximum mean discrepancy (MMD) between …

Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems

Dengfei Zeng, Lijian Jiang, Shuyu Sun, Dunhui Xiao

A likelihood-free transport filtering method is proposed based on the couplings between state and observation variables. By exploiting a block-triangular structure in the transport map, the analysis step of filtering is reformulated as the minimization of the maximum mean discrepancy (MMD) between …

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Reinforcement learning based data assimilation for unknown state model

Ziyi Wang, Lijian Jiang, Xinguang He

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Decoupling scales via localized subspace iteration and temporal splitting for multiscale parabolic equations

Eric T. Chung, Lijian Jiang, Mengnan Li, Yajun Wang

Simulating diffusion in heterogeneous media presents a significant computational challenge, as resolving microscopic physical scales traditionally demands excessively fine computational grids. To overcome this barrier, we extend the Localized Subspace Iteration (LSI) framework to multiscale parabolic equations. The proposed method constructs optimal, …

Decoupling scales via localized subspace iteration and temporal splitting for multiscale parabolic equations

Eric T. Chung, Lijian Jiang, Mengnan Li, Yajun Wang

Simulating diffusion in heterogeneous media presents a significant computational challenge, as resolving microscopic physical scales traditionally demands excessively fine computational grids. To overcome this barrier, we extend the Localized Subspace Iteration (LSI) framework to multiscale parabolic equations. The proposed method constructs optimal, …

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Novel fault area identification scheme for VSC-HVDC grids based on traveling-wave dispersion coefficient

Biao Li, Lixuan Zhu, Dawei Chen, Lijian Jiang et autres

Compared with the distributed configuration of current-limiting reactors at both line terminals, centralized installation of reactors at converter station outlets in VSC-based DC grids allows not only reduced construction costs but also enhanced economic efficiency. To address the incompatibility of boundary-based single-ended …

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Ultra-Short-Term Wind Power Forecasting Method Based on Wind Lidar Error Correction

Biao Li, Lixuan Zhu, Shaokun Deng, Lijian Jiang et autres

Accurate ultra-short-term wind power forecasting is essential for the stable integration of offshore wind power into the grid. However, standard Numerical Weather Prediction (NWP) data often lacks hub-height observations and suffers from inherent non-linear biases. This paper proposes a systematic integration framework …

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