Qingyu Qu
I am a master student in machine learning and industrial control systems at Zhejiang University. My research interest focuses on the intersection of machine learning and dynamical systems. I participated in GSoC 2023 with SciML under the NUMFOCUS umbrella.
Session
This talk presents recent advances in efficient boundary value problem solving within the SciML ecosystem, focusing on extending collocation-based and nonlinear programming formulations implemented in BoundaryValueDiffEq.jl. We demonstrate how BVPs can be reformulated as structured optimization problems, enabling seamless integration with SciML’s differentiable programming stack and modern optimization tools. Building on this perspective, we introduce strategies for improving performance and scalability, including structure-aware discretizations, GPU-parallel ensemble solving, and algorithmic techniques that bridge differential equation solvers with optimal control and dynamical optimization pipelines. We further show how these methods enable new application workflows, where differential equations, parameter estimation, and optimal control problems are solved within a unified composable framework.