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.

翻译：我们提出了一种系统，用于优化受辐射压力（即光对物体运动的影响）约束的参数化设计。这在航天器设计中最为相关，因为辐射压力是主要的非保守力机制，在约800公里高度以上尤其如此。尽管其重要性显著，但高保真辐射压力建模的高计算成本限制了其在大规模航天器设计、优化和空间态势感知应用中的使用。我们通过仿真、表示和优化三个方面的创新实现了这一目标：首先，提出了一种实用的受计算机图形学启发的辐射压力蒙特卡洛（MC）仿真方法。该仿真具有高度并行性，采用重要性采样和下一事件估计来降低方差，并能仿真整个设计族而非如先前工作中仅针对单个航天器。其次，我们引入神经网络作为从设计参数到力的表示方法。这种从仿真数据中学习的神经代理模型本质上是可微的，其查询速度比完整MC仿真快数个数量级。第三，我们展示了逆辐射压力设计的优化应用，例如寻找能最小化航行时间、在给定目标端点下最大化接近度、最小化推进器燃料消耗、训练任务控制策略或分配地外计算资源的最佳几何形状、材料或操作参数。