Yongcun Song

Lehrstuhl für Dynamics, Control, Machine Learning and Numerics (Alexander von Humboldt-Professur)

FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning (2025) Song Y, Wang Z, Zuazua Iriondo E Journal article An Augmented Lagrangian Method for State Constrained Linear Parabolic Optimal Control Problems (2024) Wang H, Yu C, Song Y Journal article The hard-constraint PINNs for interface optimal control problems (2024) Lai MC, Song Y, Yuan X, Yue H, Zeng T Unpublished / Preprint Approximate and Weighted Data Reconstruction Attack in Federated Learning (2024) Wang Z, Song Y, Zuazua Iriondo E Unpublished / Preprint Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems (2024) Song Y, Yuan X, Yue H Unpublished / Preprint The ADMM-PINNs algorithmic framework for nonsmooth PDE-constrained optimization: a deep learning approach (2024) Song Y, Yuan X, Yue H Unpublished / Preprint A numerical approach to the optimal control of thermally convective flows (2023) Song Y, Yuan X, Yue H Journal article, Original article A Two-Stage Numerical Approach for the Sparse Initial Source Identification of a Diffusion-Advection Equation (2023) Biccari U, Song Y, Yuan X, Zuazua Iriondo E Journal article An ADMM numerical approach to linear parabolic state constrained optimal control problems (2020) Song Y Journal article, Original article AN INEXACT UZAWA ALGORITHMIC FRAMEWORK FOR NONLINEAR SADDLE POINT PROBLEMS WITH APPLICATIONS TO ELLIPTIC OPTIMAL CONTROL PROBLEM (2019) Song Y Journal article