Authors: Aniket Jivani (University of Michigan), Hongfan Chen (University of Michigan), Xun Huan (University of Michigan), Yang Chen (University of Michigan), Bart van der Holst (University of Michigan), Shasha Zou (University of Michigan), Zhenguang Huang (University of Michigan), Nishtha Sachdeva (University of Michigan), Ward Manchester (University of Michigan), Gabor Toth (University of Michigan)

The Space Weather Modelling Framework (SWMF) offers efficient and flexible sun-to-earth simulations based on coupled first principles and/or empirical models. This encompasses computing the quiet solar wind, generating a coronal mass ejection (CME), propagating the CME through the heliosphere, and calculating the magnetospheric impact via geospace models. Quantifying parametric uncertainty at each stage is critical for accurate and reliable predictions. Stage 1 of the NextGen SWMF Project focused on modeling the quiet solar wind and performing global sensitivity analysis (GSA) to identify key influential parameters in the Alfvén Wave Solar atmosphere Model (AWSoM). In Stage 2, we model CMEs by parametrizing a magnetic flux rope and launching it into the precomputed ambient solar wind. It is highly beneficial from a computational standpoint to analyze early-stage data products such as synthetic white light images to predict arrival time and impact at 1au. The computational expense of running these models makes it critical to augment a limited ensemble of simulations with emulators or surrogate models for uncertainty quantification (UQ). These surrogate models should offer good prediction and extrapolation capabilities and generalize well to unseen data. Here, we discuss application of well-known emulators such as the Proper Orthogonal Decomposition and Operator Inference for white light images. We summarize the results and their potential use for forward and inverse UQ.