GEM: Explorative Global-To Kinetic-Scale Modeling of Collisionless Shocks Using Physics-Informed Data Mining and Machine Learning

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Collisionless shock waves occur in space plasmas throughout the Universe and are one of the leading mechanisms considered responsible for accelerating cosmic rays. Understanding the physics at work in collisionless shocks in space plasmas has broad impacts on a number of fundamental scientific fields, from solar and space physics to planetary sciences and astrophysics. At Earth, a bow shock forms from the deflection of solar wind plasma around the planet's magnetic field, offering an ideal natural laboratory to explore the nature of such collisionless shocks. This effort promises to use state-of-the-art machine learning and artificial intelligence techniques and tools applied to 10,000 satellite crossings of Earth's bow shock. The intent is to develop a series of data-driven and physics-informed models of collisionless shocks that not only well capture the microscopic to macroscopic nature of Earth's own bow shock but may also be applied to a better understanding of shocks at other planetary systems (including exo-planets in other stellar systems), solar and stellar shocks, and other more extreme astrophysical shocks. If successful, these models will prove transformative by enabling us to produce and explore simulated examples of shocks in systems that we have no immediate access to, establishing a new interdisciplinary connection spanning between solar and magnetospheric space plasma physics and planetary and astrophysics. Furthermore, the effort involves a diverse leadership team and will provide dedicated research opportunities for underserved academic communities.The research centers around the development of and exploration using a pair of parameterized, empirical models of i) Earth's three-dimensional (3D), global bow shock, and ii) general collisionless shocks at ion-kinetic scales. Models will be developed using advanced data mining and state-of-the-art physics-informed machine learning tools applied to NASA's Magnetospheric Multiscale (MMS) and solar wind datasets. MMS' greater dataset is ideal for machine learning applications since it is accompanied by a dataset of "scientist-in-the-loop" (SITL) reports, which effectively serve as an expert-determined and easily minable validation dataset. Once developed as part of the research, model i) promises to be the first empirical (i.e., computationally inexpensive), parameterized, global-3D model of Earth's bow shock to accurately capture the critical quasi-parallel and quasi-perpendicular shock regimes and their respective distortion of the global bow shock surface. Model ii) will be developed by coupling machine learning applications to fundamental physical principles of collisionless shocks in space plasmas, establishing a genuine physics-informed machine learning model to ideally offer accurate predictions of not only shocks like those used to train the model (i.e., Earth's bow shock under a variety of driving conditions) but also extrapolative predictive capabilities for other planetary (and exoplanetary) bow shocks, collisionless solar shocks, interplanetary and heliospheric (and astrospheric) shocks, and other collisionless astrophysical shocks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.