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Abigail Hsu
Abigail Hsu
Dept. of Applied Mathematics and Statistics, Stony Brook University
Verified email at stonybrook.edu
Title
Cited by
Cited by
Year
Effective performance portability
SL Harrell, J Kitson, R Bird, SJ Pennycook, J Sewall, D Jacobsen, ...
2018 IEEE/ACM International Workshop on Performance, Portability and …, 2018
392018
Analysis of NIF scaling using physics informed machine learning
A Hsu, B Cheng, PA Bradley
Physics of Plasmas 27 (1), 012703, 2020
212020
Analysis of NIF scaling using physics informed machine learning
A Hsu, B Cheng, P Bradley
Bulletin of the American Physical Society 64, 2019
212019
Front-tracking methods
D She, R Kaufman, H Lim, J Melvin, A Hsu, J Glimm
Handbook of Numerical Analysis 17, 383-402, 2016
212016
Performance Portability Challenges for Fortran Applications
A Hsu, DN Asanza, JA Schoonover, Z Jibben, NN Carlson, R Robey
2018 IEEE/ACM International Workshop on Performance, Portability and …, 2018
102018
Beyond optimization—supervised learning applications in relativistic laser-plasma experiments
J Lin, Q Qian, J Murphy, A Hsu, A Hero, Y Ma, AGR Thomas, ...
Physics of Plasmas 28 (8), 083102, 2021
82021
Scaling laws for partially developed turbulence
A Hsu, R Kaufman, J Glimm
arXiv preprint arXiv:2003.06968, 2020
12020
Feature analysis in relativistic laser-plasma experiments utilizing machine learning methods
J Lin, Q Qian, J Murphy, A Hsu, A Hero, AGR Thomas, K Krushelnick
arXiv e-prints, arXiv: 2011.05866, 2020
2020
Towards predicting electron beam charge upon phase control in laser wakefield accelerators using supervised learning techniques
J Lin, Q Qian, J Murphy, A Hsu, A Thomas, K Krushelnick, A Hero
APS Division of Plasma Physics Meeting Abstracts 2020, TO03. 006, 2020
2020
A Taylor-Microscale Transport Model for RANS
D Israel, A Hsu, J Rudolph
APS Division of Fluid Dynamics Meeting Abstracts, Q29. 009, 2017
2017
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Articles 1–10