Photons x Force: Differentiable Radiation Pressure Modeling

2026-02-11

AI Summary

This work proposes an efficient optimization framework to address the computational intractability of high-fidelity solar radiation pressure modeling, which hinders large-scale spacecraft design optimization. By integrating graphics-inspired parallel Monte Carlo simulations—enhanced with importance sampling and next-event estimation—the method accelerates radiation force computation. A differentiable neural surrogate model is then constructed to enable rapid force queries, and this surrogate is embedded within a differentiable optimization pipeline to facilitate multi-objective inverse design. The resulting approach dramatically improves computational efficiency, enabling effective optimization of complex mission objectives such as flight time, fuel consumption, and control strategies.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• radiation pressure
• spacecraft design
• computational cost
• optimization
• space situational awareness

Innovation

• differentiable radiation pressure
• Monte Carlo simulation
• neural proxy model
• inverse design optimization
• spacecraft parametric optimization