Thomas M., Jr. Antonsen

About

Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, serving as a Professor of Physics, Electrical and Computer Engineering, and Electrophysics. Born in Hackensack, New Jersey in 1950, he completed his Bachelor's degree in electrical engineering at Cornell University in 1973, followed by his Master's and Ph.D. degrees in 1976 and 1977, respectively. He was a National Research Council postdoctoral fellow at the Naval Research Laboratory and a research scientist at MIT's Research Laboratory of Electronics before joining the University of Maryland faculty in 1984. His research interests include the theory of magnetically confined plasmas, the design of high-power sources of coherent radiation, nonlinear dynamics in fluids, and the interaction of intense laser pulses with plasmas. Throughout his career, Professor Antonsen has held visiting appointments at prominent institutions such as the Institute for Theoretical Physics at U.C.S.B., the Ecole Polytechnique Federale de Lausanne, and the Institute de Physique Theorique at Palaiseau, France. He has been recognized with numerous awards, including the American Physical Society James Clerk Maxwell Award in 2023, the IEEE Marie Sklodowska-Curie Award in 2022, and the University of Maryland Distinguished University Professor in 2017. His contributions to plasma physics, vacuum electronics, and computational design tools have been acknowledged by his election as a Fellow of both the IEEE and…

Research topics

• Computer Science
• Machine Learning
• Physics
• Quantum mechanics
• Optics
• Artificial Intelligence
• Mathematics
• Arithmetic
• Engineering
• Electrical engineering

Selected publications

• Principles of Free Electron Lasers

  Springer eBooks · 2023 · 54 citations

  Senior authorCorresponding

  This book presents a comprehensive description of the physics of free-electron lasers starting from the fundamentals.

  DOI

• Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

  Physical Review Letters · 2022 · 49 citations

  Forecasting the dynamics of large, complex, sparse networks from previous time series data is important in a wide range of contexts. Here we present a machine learning scheme for this task using a parallel architecture that mimics the topology of the network of interest. We demonstrate the utility and scalability of our method implemented using reservoir computing on a chaotic network of oscillators. Two levels of prior knowledge are considered: (i) the network links are known, and (ii) the netw…

  DOI

• Hybridizing traditional and next-generation reservoir computing to accurately and efficiently forecast dynamical systems

  Chaos An Interdisciplinary Journal of Nonlinear Science · 2024-06-01 · 20 citations

  preprintOpen access

  Reservoir computers (RCs) are powerful machine learning architectures for time series prediction. Recently, next generation reservoir computers (NGRCs) have been introduced, offering distinct advantages over RCs, such as reduced computational expense and lower training data requirements. However, NGRCs have their own practical difficulties, including sensitivity to sampling time and type of nonlinearities in the data. Here, we introduce a hybrid RC-NGRC approach for time series forecasting of dy…

  PublisherOA PDFDOI

• Topology and manipulation of scattering singularities in complex non-Hermitian systems: Two-channel case

  Physical Review Research · 2025-04-25 · 11 citations

  articleOpen access

  The control of wave scattering in complex non-Hermitian settings is an exciting subject—often challenging the creativity of researchers and stimulating the imagination of the public. Successful outcomes include invisibility cloaks, wavefront shaping protocols, active metasurface development, and more. At their core, these achievements rely on our ability to engineer the resonant spectrum of the underlying physical structures, which is conventionally accomplished by carefully imposing geometrical…

  PublisherOA PDFDOI

• Efficient Calculation of the Self-Magnetic Field, Self-Force, and Self-Inductance for Electromagnetic Coils

  IEEE Transactions on Magnetics · 2024-09-06 · 9 citations

  articleSenior author

  The design of electromagnetic coils may require evaluation of several quantities that are challenging to compute numerically. These quantities include Lorentz forces, which may be a limiting factor due to stresses; the internal magnetic field, which is relevant for determining stress as well as a superconducting coil’s proximity to its quench limit; and the inductance, which determines stored magnetic energy and dynamics. When computing the effect on one coil due to the current in another, these…

  PublisherDOI

Recent grants

• Ultra-Intense Laser Pulse Propagation in Gas, Cluster-Gasses and Plasma

  NSF · $210k · 2003–2007

Frequent coauthors

• B. Levush

  United States Naval Research Laboratory

  419 shared

• Edward Ott

  230 shared

• D. Chernin

  Leidos (United States)

  170 shared

• Gregory S. Nusinovich

  University of Maryland, College Park

  165 shared

• Alexander N. Vlasov

  Naval Research Laboratory Electronics Science and Technology Division

  159 shared

• Steven M. Anlage

  University of Maryland, College Park

  116 shared

• Simon J. Cooke

  United States Naval Research Laboratory

  107 shared

• Igor A. Chernyavskiy

  United States Naval Research Laboratory

  101 shared

Education

• Ph.D., Engineering

  Cornell University

• M.S., Engineering

  Cornell University

• B.S., Electrical Engineering

  Cornell University

Awards & honors

• American Physical Society James Clerk Maxwell Award (2023)
• IEEE Marie Sklodowska-Curie Award (2022)
• University of Maryland Distinguished University Professor (2…
• John R. Pierce Award for Excellence in Vacuum Electronics (2…
• Fellow of the Institute of Electrical and Electronics Engine…

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