Članki z zahtevami za javni dostop - Allon PercusVeč o tem
Na voljo nekje: 12
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
M Schwarzer, B Rogan, Y Ruan, Z Song, DY Lee, AG Percus, VT Chau, ...
Computational Materials Science 162, 322-332, 2019
Zahteve: US Department of Energy
Machine learning for graph-based representations of three-dimensional discrete fracture networks
M Valera, Z Guo, P Kelly, S Matz, VA Cantu, AG Percus, JD Hyman, ...
Computational Geosciences 22, 695-710, 2018
Zahteve: US National Science Foundation, US Department of Energy
Partitioning networks with node attributes by compressing information flow
LM Smith, L Zhu, K Lerman, AG Percus
ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (2), 1-26, 2016
Zahteve: US National Science Foundation
Degree correlations amplify the growth of cascades in networks
XZ Wu, PG Fennell, AG Percus, K Lerman
Physical Review E 98 (2), 022321, 2018
Zahteve: US Department of Defense
The transsortative structure of networks
SC Ngo, AG Percus, K Burghardt, K Lerman
Proceedings of the Royal Society A 476 (2237), 20190772, 2020
Zahteve: US Department of Defense
Clique densification in networks
H Pi, K Burghardt, AG Percus, K Lerman
Physical Review E 107 (4), L042301, 2023
Zahteve: US Department of Defense
The emergence of heterogeneous scaling in research institutions
KA Burghardt, Z He, AG Percus, K Lerman
Communications Physics 4 (1), 189, 2021
Zahteve: US Department of Defense
Addressing quantum’s “fine print” with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks
JM Henderson, J Kath, JK Golden, AG Percus, D O’Malley
Scientific Reports 14 (1), 3592, 2024
Zahteve: US Department of Energy
Harnessing Uncertainty through Functional Data Analysis in Gas Breakthrough Data
W Gao, L Gianuca, TK Kong, J Lee, K Sheldon, AG Percus, JDH Hyman, ...
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2023
Zahteve: US Department of Energy
Learning Gas Transport through Fracture Networks from Multi-Fidelity Data [Slides]
D Berry, J Kath, S Lodhy, A Ly, Y Shi, A Percus, JDH Hyman, KR Moran, ...
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2022
Zahteve: US Department of Energy
Using Machine Learning Approaches to Predict Atomic-Scale Glass Failure in Environmental Conditions.
V Lloyd, S Lu, J Peña, A Percus, C Wang, R Zhao, TJ Hardin, M Wilson
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021
Zahteve: US Department of Energy
Heterogeneous Scaling of Research Institutions
KA Burghardt, Z He, AG Percus, K Lerman
arXiv preprint arXiv:2001.08734, 2020
Zahteve: US Department of Defense
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