The AIMFAHR Project

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Artificial Intelligence (AI) techniques, particularly Machine Learning (ML), have undergone significant growth in heliophysics research in recent years. Various ML models have emerged, some outperforming empirical and physics-based models while significantly reducing computational time. The Artificial Intelligence Modeling Framework for Advancing Heliophysics Research (AIMFAHR) project is an initiative aimed at integrating community-wide AI efforts into a unified AI modeling framework, advancing system-of-systems science in Sun-Earth interactions and enhancing the predictability of space weather hazards. We selected data-driven models of the magnetosheath, cusps, auroral precipitation, field-aligned currents (FACs), ionospheric electrodynamics, and thermospheric density as the initial set of AIMFAHR base models. We simulated geomagnetic storms on 4 Jan 2023, 6 May 2023, and 11 May 2024, selected by the Machine Learning-based Geospace Environment Modeling (MLGEM) resource group at the Geospace Environment Modeling (GEM) workshop.

Expected benefits

These initial efforts provide valuable insights for future AIMFAHR activities, including ML model coupling, knowledge transfer between models, uncertainty quantification, and research-to-operation transitions.

System outputs

The AIMFAHR models reveal the storm responses of various geospace systems from a data-driven perspective, including the spatiotemporal variation of the magnetopause reconnection line and its global dayside reconnection rate; cusp motions and the evolution of cusp ion energy dispersions; auroral boundary motions and variation in global auroral spectrums; increases in FACs and ionospheric potentials; and enhanced Joule heating in the upper atmosphere.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported