![[2502.05351] Deep Generative model that uses physical quantities to generate and retrieve solar magnetic active regions](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2502.05351] Deep Generative model that uses physical quantities to generate and retrieve solar magnetic active regions
Summary
This article presents a deep generative model that utilizes physical quantities to generate and retrieve solar magnetic active regions, enhancing the interpretability of generative AI in scientific contexts.
Why It Matters
The integration of generative models with physical data allows for more accurate simulations and retrieval of solar magnetic patches, bridging the gap between artificial intelligence and scientific inquiry. This advancement can significantly impact research in heliophysics and related fields.
Key Takeaways
- Generative models can produce high-quality solar magnetic patches based on physical properties.
- The study combines GANs and SVMs to create a physically interpretable model.
- Generative AI can be utilized for scientific data interrogation beyond traditional applications.
Astrophysics > Solar and Stellar Astrophysics arXiv:2502.05351 (astro-ph) [Submitted on 7 Feb 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Deep Generative model that uses physical quantities to generate and retrieve solar magnetic active regions Authors:Subhamoy Chatterjee, Andres Munoz-Jaramillo, Anna Malanushenko View a PDF of the paper titled Deep Generative model that uses physical quantities to generate and retrieve solar magnetic active regions, by Subhamoy Chatterjee and 1 other authors View PDF HTML (experimental) Abstract:Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models have encountered great skepticism in scientific domains due to the disconnection between generative latent vectors and scientifically relevant quantities. In this study, we integrate three types of machine learning models to generate solar magnetic patches in a physically interpretable manner and use those as a query to find matching patches in real observations. We use the magnetic field measurements from Space-weather HMI Active Region Patches (SHARPs) to train a Generative Adversarial Network (GAN). We connect the physical properties of GAN-generated images with their latent vectors to train Support Vector Machines (SVMs) that do mapping between physical and latent spaces. These produc...