Ben Zhu
Assistant Professor of Applied Physics
Ben Zhu is a theoretical and computational physicist whose research advances our understanding of fusion and laboratory plasmas. His work focuses on magnetic fusion energy (MFE), with particular emphasis on the nonlinear dynamics of magnetized plasmas across multiple spatial and temporal scales.
Zhu investigates phenomena such as plasma instabilities, turbulence, transport barriers, bifurcation processes, and particle and heat exhaust in magnetic confinement devices including tokamaks and stellarators. His research integrates both fluid and kinetic theories, employing high-performance computing and advanced numerical methods to model boundary plasma behavior. Zhu is also at the forefront of applying machine learning and artificial intelligence (ML/AI) to plasma physics, developing neural-network-based kinetic closures and surrogate models for tokamak control and predictive modeling.
Zhu received his BS degree in Applied Physics from the University of Science and Technology of China in 2008, his MS degree in Applied Science from the University of California, Davis in 2009, and his PhD in Physics from Dartmouth College in 2017. His doctoral work explored the interplay between turbulence, transport, and spontaneously generated shear flows at the tokamak edge using global three-dimensional two-fluid electromagnetic simulation. After serving as a research associate at Dartmouth College, he joined Lawrence Livermore National Laboratory (LLNL) in 2018 and became a staff scientist in 2020. He was honored with the U.S. Department of Energy (DOE) Early Career Award in 2024.
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Research Areas
Plasma physics; numerical modeling of magnetized plasma; turbulence and transport processes; fusion exhaust and control strategies; ML/AI applications in fusion energy science.