The scientific goal of this project is to develop a dynamical heliospheric modeling framework that will reproduce the global, large scale solar wind evolution through a more accurate, local…

This federal contract award is a $1,176,521 grant from a civilian federal agency to the University of California, Los Angeles (UCLA) for the development of a dynamical heliospheric modeling framework. The goal is to reproduce global, large-scale solar wind evolution through a more accurate, local description of kinetic dynamics in the heliosphere. The project integrates expertise from kinetic plasma modeling, artificial intelligence/machine learning, and in-situ solar wind observations to address the challenge of modeling kinetic processes across vastly different spatial and temporal scales. This grant does not have a set-aside designation. UCLA, as a state-controlled institution of higher learning and 1862 Land Grant College, frequently serves as a prime contractor for federal research and technical services. The parent organization is the State of California Controllers Office, which has a history of securing federal contracts and grants across diverse domains like public health, scientific research, and technological innovation.

The scientific goal of this project is to develop a dynamical heliospheric modeling framework that will reproduce the global, large scale solar wind evolution through a more accurate, local description of kinetic dynamics in the heliosphere. To this end IT integrates and combines expertise from theoretical and numerical kinetic plasma modeling, artificial intelligence/machine learning (AI/ML), and in-situ observations of the solar wind. The evolution of particle distribution functions with heliocentric distance is controlled both by large scale driving and local kinetic effects, with the latter including processes such as instabilities, wave-particle interactions, magnetic reconnection and turbulence. Several orders of magnitude in space and time separate kinetic scales in the solar wind from the scales of the global evolution from the sun. For this reason, modeling of kinetic processes has clustered at the two extremes of the scale separation: (1) large scale models focus on large scale evolution of particle velocity distribution functions (vdfs), aiming at reproducing observations at 1 AU and beyond, but significantly approximating the kinetic solar wind dynamics that drive VDF evolution (2) fully kinetic (e.g. particle in cell- PIC) models focus on kinetic scale processes (magnetic reconnection, turbulence, waves and instabilities), accurately reproducing local dynamics but prevented, from the inescapable cost of PIC simulations, from addressing their coupling with larger scale dynamics. Our new modeling framework will be built by harnessing the power of state of the art kinetic simulations of the solar wind, local solar wind observations at different heliocentric distances and AI/ML techniques. This proposal focuses on the development, validation and deployment of a global solar wind model that globally evolves solar wind models on the basis of realistically modeled local kinetic processes. Therefore, the proposal contributes to three scientific objectives of the heliophysics decadal survey: "determine the origins of the sun's activity and predict the variations in the space environment", "determine the interaction of the sun with the solar system and the interstellar medium", and "discovering and characterizing fundamental processes that occur both within the heliosphere and throughout the universe."