From the 1 of 5 linked papers with an AI index.
5 papers
Forked Physics-Informed Neural Networks for Non-Markovian Open Quantum Dynamics and Control
Zhao-Wei Wang, Kai-Yu Yuan, Feng-Hua Ren +1
The paper introduces a forked physics-informed neural network (FPINN) that jointly simulates and controls non‑Markovian open quantum systems by decoupling optimization objectives,…
Enhancing Presolve in Mixed Integer Programming by Combining Probing and Dual Fixing
Zhao-Wei Wang, Wei-Kun Chen, Yu-Hong Dai
Probing and dual fixing are two powerful presolve techniques in mixed integer programming (MIP) solvers. Probing tentatively sets some binary variables to 0 or 1, applies linear co…
Exploiting Variable Implications in Presolve for Mixed Integer Programming
Wei-Kun Chen, Chang-Long Li, Zhao-Wei Wang +3
Presolve for mixed integer programming (MIP) problems aims to eliminate redundant information, strengthen the formulation, and extract useful structural information for the subsequ…
Forked Physics Informed Neural Networks for Coupled Systems of Differential equations
Zhao-Wei Wang, Zhao-Ming Wang
Solving coupled systems of differential equations (DEs) is a central problem across scientific computing. While Physics Informed Neural Networks (PINNs) offer a promising, mesh-fre…
Machine-Learning-Assisted Pulse Design for State Preparation in a Noisy Environment
Zhao-Wei Wang, Hong-Yang Ma, Yun-An Yan +2
High-precision quantum control is essential for quantum computing and quantum information processing. However, its practical implementation is challenged by environmental noise, wh…