arXiv subjects

Dustin Kempton

Publications and source records attributed to Dustin Kempton.

10 recordsLinked to original sources

arXiv2026

Developing Machine Learning Models of Subgrid Turbulent Transport for Quiet Sun 3D Radiative Hydrodynamic Simulations

Numerical modeling of solar plasma dynamics is affected by the resolution of the computational grid. This often requires the estimation of subgrid processes related to the small-scale flow turbulence, as these processes play a critical role in momentum transport and energy dissipation. In this work, we investigate the use of deep learning techniques as surrogate models for subgrid turbulent transport in realistic hydrodynamic simulations of the quiet Sun. We describe the development of a 3D Convolutional Neural Network (CNN) to capture spatial dependencies in 3D velocity fields, leveraging different activation functions, as well as different architectural designs. We specifically focus on the prediction of Reynolds stress tensor components. The resultant model integrates velocity vector components and scalar features, such as plasma density, to enhance prediction accuracy. We compare the 3DCNN model to other types of models, such as a Multilayer Perceptron (MLP) and physics-based Gradient and Smagorinsky models, and show that the final model design reconstructs the Reynolds stress tensor components more accurately. Specifically, a 3DCNN model achieves an average improvement of ~31% on diagonal components and ~8% on the off-diagonal components of the stress tensor. Additionally, we show that applying a logarithmic data transformation of the target stress tensor components, to handle heavily skewed data, improves model performance. Results demonstrate the potential of deep learning, particularly CNNs, to approximate Reynolds stress tensor components for the upper solar convection zone and lower atmosphere, making them a viable candidate for modeling subgrid processes and a promising alternative to traditional turbulence models.

astro-ph.SR↗

arXiv2026

Cluster-Weighted Training of Deep Surrogate Models for Subgrid Turbulent Transport

Turbulence in the solar interior and atmosphere plays a crucial role in energy transport, yet modeling its subgrid-scale effects remains a major challenge. This study leverages machine learning (ML) models to predict components of the Reynolds stress tensor using high-resolution StellarBox simulations of the quiet Sun. Previously, we have compared a Multi-Layer Perceptron (MLP) and a 3D Convolutional Neural Network (CNN) against physics-based baselines to achieve a lower Mean Squared Error (MSE) and better generalization across various heights and depths in the solar atmosphere. To enhance learning, in this work, we investigate cluster-weighted training using K-Means and Hierarchical Agglomerative Clustering (HAC). By weighing the loss function based on cluster-specific prediction errors, we direct the model's attention to high-error regions. It significantly improves CNN performance, achieving 34% lower MSE and a significantly higher R2 score indicating that integrating deterministic clustering with ML is a promising technique for modeling subgrid turbulence, in particular, and regression in diverse environments, in general.

astro-ph.SR↗

arXiv2026

Describing the swdatatoolkit: A Space Weather Data Analysis Library

swdatatoolkit is a Python-based scientific software library designed to support the acquisition, preprocessing, and analysis of solar and space weather data. The toolkit consolidates functionality across multiple domains, including data downloading from established heliophysics sources, image preprocessing, edge detection, image texture quantification, magnetic field analysis, and the derivation of higher-level parameters commonly used in solar physics research. Its modular structure reflects the heterogeneous nature of space weather data and enables reproducible, extensible workflows for both exploratory analysis and machine-learning-driven studies. This paper presents an overview of the library's available capabilities, its scientific motivations, and its role in the broader space weather research ecosystem.

astro-ph.IM↗

arXiv2026

Machine Learning-Ready Data Sets for the Analysis and Nowcasting of Atmospheric Radiation at Aviation Altitudes

Nowcasting and forecasting of the radiation environment in the Earth's lower atmosphere are critical for the safety of aircraft and spacecraft crews and passengers. Currently, this problem is addressed by employing statistical and physics-based models that take into account particle transport and precipitation. However, given the increased number of radiation measurements available to the community, it is possible to start developing data-driven approaches. We prepared Machine Learning-ready (ML-ready) datasets to nowcast the effective dose rates at aviation altitudes. The presented datasets contain 92,476 individual measurements from 589 flights obtained by the Automated Radiation Measurements for Aerospace Safety (ARMAS) experiment from 2013 to 2023. The ARMAS measurements are augmented with the properties of the Geospace environment, such as solar soft X-ray and proton fluxes, solar wind properties, secondary cosmic ray neutrons, space weather indexes, and global solar activity indicators (such as daily sunspot number). ARMAS data are separated into three partitions, ensuring that (1) the data points from a single flight remain within the same partition, and (2) each partition samples the flight locations and Geospace environment conditions equally. Several versions of the datasets allow predictions based on point-in-time measurements and use up to 24 hours of Geospace parameter history. The test of the use case demonstrates a possibility of nowcasting ARMAS measurements with accuracies slightly better than the considered physics-based models. The publicly available ML-ready datasets could serve as the first step in data preparation for ML-driven nowcasting and forecasting of the radiation environment.

physics.ao-ph↗

arXiv2025

Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator

The solar wind, a continuous stream of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Variations such as high-speed streams and coronal mass ejections can disrupt satellites, power grids, and communications, making accurate modeling essential for space weather forecasting. While 3D magnetohydrodynamic (MHD) models are used to simulate and investigate these variations in the solar wind, they tend to be computationally expensive, limiting their usefulness in investigating the impacts of boundary condition uncertainty. In this work, we develop a surrogate for steady state solar wind modeling, using a Spherical Fourier Neural Operator (SFNO). We compare our model to a previously developed numerical surrogate for this task called HUX, and we show that the SFNO achieves comparable or better performance across several metrics. Though HUX retains advantages in physical smoothness, this underscores the need for improved evaluation criteria rather than a flaw in SFNO. As a flexible and trainable approach, SFNO enables efficient real-time forecasting and can improve with more data. The source code and more visual results are available at https://github.com/rezmansouri/solarwind-sfno-velocity.

cs.LG↗

arXiv2025

Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator

The solar wind, a continuous outflow of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is critical for space weather forecasting, but traditional three-dimensional magnetohydrodynamic (MHD) models are computationally expensive, limiting rapid exploration of boundary condition uncertainties. We introduce the first autoregressive machine learning surrogate for steady-state solar wind radial velocity using the Spherical Fourier Neural Operator (SFNO). By predicting a limited radial range and iteratively propagating the solution outward, the model improves accuracy in distant regions compared to a single-step approach. Compared with the numerical HUX surrogate, SFNO demonstrates superior or comparable performance while providing a flexible, trainable, and data-driven alternative, establishing a novel methodology for high-fidelity solar wind modeling. The source code and additional visual results are available at https://github.com/rezmansouri/solarwind-sfno-velocity-autoregressive.

cs.LG↗

arXiv2025

Nowcasting of Aviation Radiation Using Geospace Environment Properties: A Machine Learning Approach

Radiation exposure at aviation altitudes presents significant health risks to aircrews due to the cumulative effects of ionizing radiation. Physics-based models estimate radiation levels based on geophysical and atmospheric parameters, but often struggle to capture the highly dynamic and complex nature of the radiation environment, limiting their real-time predictive capabilities. To address this challenge, we investigate machine learning (ML) methods to enhance real-time radiation nowcasting. Leveraging newly compiled ML-ready datasets, publicly available at https://dmlab.cs.gsu.edu/rdp/, we train supervised models capable of capturing both linear and non-linear relationships between Geospace conditions and atmospheric radiation levels. Our experiments demonstrate that the XGBoost model achieves approximately 10 percent improvement in prediction accuracy over the considered physics-based model. Furthermore, feature importance analysis reveals that certain Geospace properties, specifically solar polar fields, solar wind properties, and neutron monitor data, are impacting the nowcast of the radiation levels at flight altitudes. These findings suggest meaningful physical relationships between the near-Earth space environment and atmospheric radiation, and highlight the potential of ML-based approaches for operational space weather applications.

physics.space-ph↗

arXiv2025

Operational and Exploration Requirements and Research Capabilities for SEP Environment Monitoring and Forecasting

Mitigating risks posed by solar energetic particles (SEPs) to operations and exploration in space and Earth's atmosphere motivates the development of advanced, synergistic approaches for monitoring, modeling, and analyzing space weather conditions. The consequences of SEPs and their interactions with the near-Earth space environment are numerous, including elevated radiation levels at aviation altitudes during major events, satellite damage, and health risks to astronauts, resulting in economic impacts and potential hazards for space exploration. This contribution will present a high-level overview of the operational requirements and research capabilities for SEP event environment monitoring and forecasting that were highlighted during a workshop at Georgia State University, held on October 16-19, 2024. Specifically, it summarizes the presented activities concerning the following: (1) Identifying needs for SEP event forecasting and nowcasting, including practical forecast timeframes; (2) Reviewing availability and coverage of the current observational data and identifying tangible data resources for research, operations and the R2O2R loop; (3) Mapping existing forecast capabilities and identifying meaningful modeling advances for research and operations.

astro-ph.SR↗

arXiv2022

Revisiting the Solar Research Cyberinfrastructure Needs: A White Paper of Findings and Recommendations

Solar and Heliosphere physics are areas of remarkable data-driven discoveries. Recent advances in high-cadence, high-resolution multiwavelength observations, growing amounts of data from realistic modeling, and operational needs for uninterrupted science-quality data coverage generate the demand for a solar metadata standardization and overall healthy data infrastructure. This white paper is prepared as an effort of the working group "Uniform Semantics and Syntax of Solar Observations and Events" created within the "Towards Integration of Heliophysics Data, Modeling, and Analysis Tools" EarthCube Research Coordination Network (@HDMIEC RCN), with primary objectives to discuss current advances and identify future needs for the solar research cyberinfrastructure. The white paper summarizes presentations and discussions held during the special working group session at the EarthCube Annual Meeting on June 19th, 2020, as well as community contribution gathered during a series of preceding workshops and subsequent RCN working group sessions. The authors provide examples of the current standing of the solar research cyberinfrastructure, and describe the problems related to current data handling approaches. The list of the top-level recommendations agreed by the authors of the current white paper is presented at the beginning of the paper.

astro-ph.IM↗

arXiv2020

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.

astro-ph.SR↗