This work presents a deep-learning approach to estimate atmospheric density
profiles for use in planetary entry guidance problems. A long short-term memory
(LSTM) neural network is trained to learn the mapping between measurements
available onboard an entry vehicle and the density profile through which it is
flying. Measurements include the spherical state representation, Cartesian
sensed acceleration components, and a surface-pressure measurement. Training
data for the network is initially generated by performing a Monte Carlo
analysis of an entry mission at Mars using the fully numerical
predictor-corrector guidance (FNPEG) algorithm that utilizes an exponential
density model, while the truth density profiles are sampled from MarsGRAM. A
curriculum learning procedure is developed to refine the LSTM network's
predictions for integration within the FNPEG algorithm. The trained LSTM is
capable of both predicting the density profile through which the vehicle will
fly and reconstructing the density profile through which it has already flown.
The performance of the FNPEG algorithm is assessed for three different density
estimation techniques: an exponential model, an exponential model augmented
with a first-order fading-memory filter, and the LSTM network. Results
demonstrate that using the LSTM model results in superior terminal accuracy
compared to the other two techniques when considering both noisy and noiseless
measurements.

本研究提出了一种深度学习方法，用于估计行星进入引导问题中的大气密度剖面。通过训练 LSTM 神经网络，学习了航天器上可用的测量数据与密度剖面之间的映射关系。该方法优于其他两种技术，在考虑到有噪声和无噪声测量时，从终端精度上对 FNPEG 算法进行了评估。

使用深度学习进行进入制导问题的密度估计

Density Estimation for Entry Guidance Problems using Deep Learning

A framework to learn a multi-modal distribution is proposed, denoted as the
Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network
structure is strictly within a quantum circuit and, as a consequence, is shown
to represent a more efficient state preparation procedure than current methods.
This methodology has the potential to speed-up algorithms, such as Monte Carlo
analysis. In particular, after demonstrating the effectiveness of the network
in the learning task, the technique is applied to price Asian option
derivatives, providing the foundation for further research on other
path-dependent options.

提出了一种学习多模态分布的框架 —— 条件量子生成对抗网络（C-qGAN），该神经网络结构严格在量子电路内，因此显示出比当前方法更有效的状态准备程序。将这一方法应用于定价亚式期权衍生品，为进一步研究其他路径依赖期权打下了基础。