Alexander A. Velberg

Alexander A. Velberg is a PhD student in nuclear science and engineering whose research lies at the interface of machine learning and nonlinear plasma dynamics. Specifically, Alex aims to develop a systematic program of data-driven equation discovery for plasma physics. His work builds on recent research demonstrating that it is possible to discover a known reduced plasma model from first-principles simulation data. Alex is exploring how to extend this methodology to the discovery of equations where some terms are unknown, namely, the data-driven discovery of so-called sub-grid closures. MATLAB’s Deep Learning Toolbox is the main research platform for this project. First-principles descriptions, including the detailed physical effects of discrete particles in plasmas, are extremely computationally intensive; therefore, the ability to accurately represent those effects in terms of an effective closure with a significantly reduced computational cost, would be transformational in terms of our ability to simulate plasmas. Alex’s work has the potential to offer these powerful capabilities and help usher in a new era of exploration in plasma physics, with applications to astrophysical plasmas and fusion energy.