发表机构
Austrian Academy of Sciences; Graz University of Technology（奥地利科学院; 格拉茨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对系外行星气候模拟，提出基于潜空间表示的神经网络嵌入方法，替代逐点预测，实现忠实且高效的3D气候建模，支持大规模集合研究。
AI 中文摘要
随着JWST和Ariel等望远镜的快速发展，迫切需要高效的3D气候模型来解释系外行星大气的观测数据。传统的三维大气环流模型（GCMs）计算强度大，促使机器学习（ML）模拟器的发展以加速模拟。最近的工作，如Plaschzug等人2026年的研究\ref{plaschzug2026accelerating}，使用密集神经网络（DNN）从输入参数（包括局部气体压力、空间坐标（经度和纬度）以及全局温度）预测局部气体温度和风。然而，该模型依赖于在特定网格点预测单个温度值（点），这可能受限于训练网格的分辨率和约束。在本工作中，我们研究了几种基于局部气体温度（$\text{T}_{\text{gas}}$）潜空间表示的替代框架，以获得这些剖面的忠实、低维表示。这是开发潜空间回归模型的第一步，为现有的逐点预测方法\ref{plaschzug2026accelerating}提供了一种结构上连贯的替代方案。通过捕捉大气数据的最优嵌入空间，我们提出的框架能够生成模拟剖面，同时保持计算效率，使其适用于大规模系外行星集合研究。
英文摘要
With the rapid advancement of telescopes like JWST and Ariel, there is an urgent need for efficient 3D climate models to interpret observations of exoplanet atmospheres. Traditional 3D general circulation models (GCMs) are computationally intensive, prompting the development of machine learning (ML) emulators to accelerate simulations. Recent work, such as that by Plaschzug et al. 2026 \cite{plaschzug2026accelerating}, uses a dense neural network (DNN) to predict local gas temperatures and winds from input parameters, including local gas pressure, spatial coordinates (longitude and latitude), and global temperature. However, this model relies on predicting individual temperature values (points) at specific grid points, which can be limited by the resolution and constraints of the training grid. In this work, we investigate a couple of alternative frameworks based on latent-space representations of local gas temperature ($\text{T}_{\text{gas}}$) to obtain a faithful, low-dimensional representation of these profiles. This represents the first step toward developing a latent space regression model, offering a structurally cohesive alternative to the existing point-wise prediction method \cite{plaschzug2026accelerating}. By capturing the optimal embedding space of atmospheric data, our proposed framework can produce simulated profiles while maintaining computational efficiency, making it suitable for large-scale exoplanet ensemble studies.
CommentsThis work has been accepted for publication in the peer-reviewed proceedings of the European Space Agency's Space Applications of Artificial Intelligence and Cognitive Engineering (SPAICE) Conference 2026
