Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Percus, Allon"'
Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its
Externí odkaz:
http://arxiv.org/abs/2412.14414
Politically divided societies are also often divided emotionally: people like and trust those with similar political views (in-group favoritism) while disliking and distrusting those with different views (out-group animosity). This phenomenon, called
Externí odkaz:
http://arxiv.org/abs/2403.16940
Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms re
Externí odkaz:
http://arxiv.org/abs/2310.02479
Autor:
Shi, Yingqi, Berry, Donald J., Kath, John, Lodhy, Shams, Ly, An, Percus, Allon G., Hyman, Jeffrey D., Moran, Kelly, Strait, Justin, Sweeney, Matthew R., Viswanathan, Hari S., Stauffer, Philip H.
Publikováno v:
Computers and Geosciences 192, 105700 (2024)
Modeling gas flow through fractures of subsurface rock is a particularly challenging problem because of the heterogeneous nature of the material. High-fidelity simulations using discrete fracture network (DFN) models are one methodology for predictin
Externí odkaz:
http://arxiv.org/abs/2306.03416
Real-world networks are rarely static. Recently, there has been increasing interest in both network growth and network densification, in which the number of edges scales superlinearly with the number of nodes. Less studied but equally important, howe
Externí odkaz:
http://arxiv.org/abs/2304.03479
Autor:
Shi, Yingqi, Berry, Donald J., Kath, John, Lodhy, Shams, Ly, An, Percus, Allon G., Hyman, Jeffrey D., Moran, Kelly, Strait, Justin, Sweeney, Matthew R., Viswanathan, Hari S., Stauffer, Philip H.
Publikováno v:
In Computers and Geosciences October 2024 192
Research collaborations provide the foundation for scientific advances, but we have only recently begun to understand how they form and grow on a global scale. Here we analyze a model of the growth of research collaboration networks to explain the em
Externí odkaz:
http://arxiv.org/abs/2101.11056
Research institutions provide the infrastructure for scientific discovery, yet their role in the production of knowledge is not well characterized. To address this gap, we analyze interactions of researchers within and between institutions from milli
Externí odkaz:
http://arxiv.org/abs/2001.08734
Network topologies can be non-trivial, due to the complex underlying behaviors that form them. While past research has shown that some processes on networks may be characterized by low-order statistics describing nodes and their neighbors, such as de
Externí odkaz:
http://arxiv.org/abs/1910.09538
Autor:
Schwarzer, Max, Rogan, Bryce, Ruan, Yadong, Song, Zhengming, Lee, Diana Y., Percus, Allon G., Chau, Viet T., Moore, Bryan A., Rougier, Esteban, Viswanathan, Hari S., Srinivasan, Gowri
Publikováno v:
Computational Materials Science 162, 322-332 (2019)
We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train on simulati
Externí odkaz:
http://arxiv.org/abs/1810.06118