NASA's Juno mission provided exquisite measurements of Jupiter's gravity
field that together with the Galileo entry probe atmospheric measurements
constrains the interior structure of the giant planet. Inferring its interior
structure range remains a challenging inverse problem requiring a
computationally intensive search of combinations of various planetary
properties, such as the cloud-level temperature, composition, and core
features, requiring the computation of ~10^9 interior models. We propose an
efficient deep neural network (DNN) model to generate high-precision
wide-ranged interior models based on the very accurate but computationally
demanding concentric MacLaurin spheroid (CMS) method. We trained a
sharing-based DNN with a large set of CMS results for a four-layer interior
model of Jupiter, including a dilute core, to accurately predict the gravity
moments and mass, given a combination of interior features. We evaluated the
performance of the trained DNN (NeuralCMS) to inspect its predictive
limitations. NeuralCMS shows very good performance in predicting the gravity
moments, with errors comparable with the uncertainty due to differential
rotation, and a very accurate mass prediction. This allowed us to perform a
broad parameter space search by computing only ~10^4 actual CMS interior
models, resulting in a large sample of plausible interior structures, and
reducing the computation time by a factor of 10^5. Moreover, we used a DNN
explainability algorithm to analyze the impact of the parameters setting the
interior model on the predicted observables, providing information on their
nonlinear relation.

通过 NASA 的 Juno 任务提供的木星重力场精确测量与伽利略入口探测仪大气测量相结合，研究表明，推断巨大星球的内部结构仍然是一项具有挑战性的逆向问题，需要对各种行星属性进行计算密集型搜索，提出了一种基于计算密集型 Maclaurin 准确椭球（CMS）方法的高精度广泛内部模型的有效深度神经网络（DNN）模型。

神经 CMS：研究木星内部的深度学习方法

NeuralCMS: A deep learning approach to study Jupiter's interior

The dominant majority of 3D models that appear in gaming, VR/AR, and those we
use to train geometric deep learning algorithms are incomplete, since they are
modeled as surface meshes and missing their interior structures. We present a
learning framework to recover the shape interiors (RoSI) of existing 3D models
with only their exteriors from multi-view and multi-articulation images. Given
a set of RGB images that capture a target 3D object in different articulated
poses, possibly from only few views, our method infers the interior planes that
are observable in the input images. Our neural architecture is trained in a
category-agnostic manner and it consists of a motion-aware multi-view analysis
phase including pose, depth, and motion estimations, followed by interior plane
detection in images and 3D space, and finally multi-view plane fusion. In
addition, our method also predicts part articulations and is able to realize
and even extrapolate the captured motions on the target 3D object. We evaluate
our method by quantitative and qualitative comparisons to baselines and
alternative solutions, as well as testing on untrained object categories and
real image inputs to assess its generalization capabilities.

该研究提出了一种 RoSI 学习框架，通过多视角和多关节图像从外部恢复 3D 模型的内部结构，并在实验中证实了该方法的泛化能力。