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：我们提出一种系统，用于优化受辐射压力影响的参数化设计，即光对物体运动的影响。该研究在航天器设计中尤为重要，在约800公里以上高度，辐射压力是主要的非保守作用力机制。尽管其重要性显著，高保真度辐射压力建模的高计算成本限制了其在大型航天器设计、优化及空间态势感知中的应用。我们通过在模拟、表征与优化三方面提出创新来实现这一目标：首先，采用一种基于计算机图形学启发的实用蒙特卡洛（MC）辐射压力模拟。该模拟具有高度并行性，通过重要性采样和下一事件估计降低方差，且可模拟整个设计系列（而非如以往仅模拟单个航天器）。其次，我们引入神经网络作为设计参数到作用力的表征模型。这种从模拟中习得的神经代理模型本质上是可微的，且查询力场的速度比完整MC模拟快数个数量级。最后，我们展示了逆辐射压力设计的优化能力，例如：求解最小化行程时间的几何、材料或运行参数；在给定终点条件下最大化接近度；最小化推进器燃料消耗；训练任务控制策略；或在外星计算中分配计算预算。