Jordan Murphy
Jordan Murphy is a Senior Astrodynamics Engineer at Slingshot Aerospace and a Ph.D. candidate in Aerospace Engineering at the University of Colorado Boulder under Professor Dan Scheeres. His expertise lies at the intersection of astrodynamics, scientific machine learning, numerical computing, and optimization. With experience spanning NASA’s Jet Propulsion Laboratory, Lawrence Livermore National Laboratory, CU Aerospace, and SpaceX, Jordan has contributed to mission design, machine learning and reinforcement learning applications, and high-performance computing for space systems. A strong advocate for the Julia programming language, he integrates cutting-edge computational techniques into astrodynamics research and development. Jordan’s current work focuses on enhancing space situational awareness, data-driven simulation methodologies, and optimal control.
Session
Accurate and efficient astrodynamics models are central to a wide range of space flight applications. The conventional approach to implementing these astrodynamics models in software often struggles with scalability and real-time performance, limiting their utility in modern space applications. In this talk, we introduce AstroForceModels.jl and AstroPropagators.jl, a suite of open-source Julia libraries designed to provide high-performance and differentiable solutions for orbit propagation.