Photons x Force: Differentiable Radiation Pressure Modeling

TL;DR

This paper introduces a differentiable radiation pressure modeling system using Monte Carlo simulation and neural networks to optimize spacecraft designs efficiently under light-induced forces.

Contribution

It presents a novel, parallel Monte Carlo simulation, neural network force representation, and optimization framework for radiation pressure effects in spacecraft design.

Findings

Neural network models query forces faster than traditional simulations.

The Monte Carlo simulation reduces variance and simulates multiple designs simultaneously.

Optimizations include minimizing travel time and fuel consumption under radiation pressure constraints.

Abstract

We propose a system to optimize parametric designs subject to radiation pressure, \ie the effect of light on the motion of objects. This is most relevant in the design of spacecraft, where radiation pressure presents the dominant non-conservative forcing mechanism, which is the case beyond approximately 800 km altitude. Despite its importance, the high computational cost of high-fidelity radiation pressure modeling has limited its use in large-scale spacecraft design, optimization, and space situational awareness applications. We enable this by offering three innovations in the simulation, in representation and in optimization: First, a practical computer graphics-inspired Monte-Carlo (MC) simulation of radiation pressure. The simulation is highly parallel, uses importance sampling and next-event estimation to reduce variance and allows simulating an entire family of designs instead of a single spacecraft as in previous work. Second, we introduce neural networks as a representation of forces from design parameters. This neural proxy model, learned from simulations, is inherently differentiable and can query forces orders of magnitude faster than a full MC simulation. Third, and finally, we demonstrate optimizing inverse radiation pressure designs, such as finding geometry, material or operation parameters that minimizes travel time, maximizes proximity given a desired end-point, minimize thruster fuel, trains mission control policies or allocated compute budget in extraterrestrial compute.