Optimizing Spacecraft Design with Differentiable Radiation Pressure Modeling

This paper introduces a system for optimizing parametric designs subject to radiation pressure, a dominant non-conservative force for spacecraft beyond 800 km altitude. The system combines a practical computer graphics-inspired Monte-Carlo simulation of radiation pressure with neural networks to represent forces from design parameters. This neural proxy model is differentiable and allows for faster querying of forces compared to full MC simulations. The system enables the optimization of inverse radiation pressure designs, such as minimizing travel time, maximizing proximity to a desired endpoint, minimizing thruster fuel, training mission control policies, or allocating compute budget in extraterrestrial compute. The Monte-Carlo simulation is highly parallel and uses importance sampling and next-event estimation to reduce variance. It also allows simulating an entire family of designs instead of a single spacecraft. The authors demonstrate the effectiveness of the system through various optimization tasks. This research has significant implications for spacecraft design, optimization, and space situational awareness applications, enabling more efficient and effective use of radiation pressure for spacecraft maneuvering and control.

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