Speaker
Description
Differentiable programming is advancing scientific computing by enabling gradients to flow through complex numerical models. In spaceflight mechanics, a field governed by nonlinear dynamics, uncertainty, and strict operational constraints, this approach opens new avenues for optimization, state estimation, uncertainty quantification, and decision-making.
In this talk, I will present our recent research applying differentiable programming to astrodynamics. We combine low- and high-order automatic differentiation (AD) across multiple contexts: from physics-based modelling and continuous refinements using NeuralODEs, to propagating uncertainties via truncated Taylor polynomials. Low-order AD computes gradients efficiently for machine learning tasks, physics-based modelling, and NeuralODE refinements. High-order derivatives, obtained via variational equations, provide coefficients for state transition tensors (STTs) and event transition tensors (ETTs), enabling accurate representation of solution flows and events. These high-order tools allow non-Gaussian uncertainty propagation and analytical approximations of high-order statistical moments with orders-of-magnitude fewer computations than traditional Monte Carlo simulations.
I will illustrate these techniques with applications in spaceflight mechanics: low-order AD for thermosphere density modelling, irregular silhouettes modelling and differentiable orbit propagators, as well as high-order AD for uncertainty quantification in mission analysis and guidance, navigation, and control. These approaches demonstrate the potential of differentiable programming for complex, high-dimensional physical systems.