collaborators

6 papers

cs.LG2026

Deep Wave Network for Modeling Multi-Scale Physical Dynamics

Alexander I. Khrabry, Edward A. Startsev, Andrew T. Powis +1

Performance of deep learning models is strongly governed by architectural capacity, with width and depth as primary controls. However, in physical-science applications, models are…

physics.plasm-ph2025

Benchmark for two-dimensional large scale coherent structures in partially magnetized ExB plasmas -- Community collaboration & lessons learned

Andrew T. Powis, Eduardo Ahedo, Alejandro Álvarez Laguna +38

Low-temperature plasmas are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a…

physics.plasm-ph2025

Accelerating kinetic plasma simulations with machine learning generated initial conditions

Andrew T. Powis, Domenica Corona Rivera, Alexander Khrabry +1

Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach co…

physics.plasm-ph2025

Three-dimensional Helical-rotating Plasma Structures in Beam-generated Partially Magnetized Plasmas

Jian Chen, Andrew T. Powis, Igor D. Kaganovich +1

Azimuthal structures emerging in beam-generated partially magnetized plasmas are investigated using three-dimensional particle-in-cell/Monte Carlo collision simulations. Two distin…

physics.plasm-ph2025

Sheath electron heating in surface wave discharges driven at microwave frequencies

Denis Eremin, Andrew T. Powis, Igor D. Kaganovich

Using fully electromagnetic particle-in-cell/Monte Carlo simulations, the electron heating due to interaction with a moving sheath is demonstrated to dominate in surface wave-drive…

cs.AI2025

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems

Alexander Khrabry, Edward Startsev, Andrew Powis +1

We propose a novel efficient architecture for learning long-term evolution in complex multi-scale physical systems which is based on the idea of separation of scales. Structures of…