Zobrazeno 1 - 10
of 1 134
pro vyhledávání: '"Chen, Yi‐Fan"'
Autor:
Liu, Xin-Yang, Parikh, Meet Hemant, Fan, Xiantao, Du, Pan, Wang, Qing, Chen, Yi-Fan, Wang, Jian-Xun
Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multi-scale structures of turbulence. Traditional recycling-based methods rely on computationally expensive precursor simulations, while
Externí odkaz:
http://arxiv.org/abs/2411.14378
Autor:
Shahane, Shantanu, Chammas, Sheide, Bezgin, Deniz A., Buhendwa, Aaron B., Schmidt, Steffen J., Adams, Nikolaus A., Bryngelson, Spencer H., Chen, Yi-Fan, Wang, Qing, Sha, Fei, Zepeda-Núñez, Leonardo
Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime. In this paper, we employ a rational neural network to accurately estimate the local smoothness of the sol
Externí odkaz:
http://arxiv.org/abs/2409.09217
Background. Wildfire research uses ensemble methods to analyze fire behaviors and assess uncertainties. Nonetheless, current research methods are either confined to simple models or complex simulations with limits. Modern computing tools could allow
Externí odkaz:
http://arxiv.org/abs/2406.08589
Configuring TiO2 into bicontinuous mesostructures greatly improves its photocatalytic efficiency. This is often ascribed to the expanded surface area. Yet, whether mesostructuring modulates TiO2's electronic structure and how that contributes to the
Externí odkaz:
http://arxiv.org/abs/2310.13306
Autor:
Pierce, Damien, Chen, Yi-fan
Traffic evacuation planning can be essential in saving lives in case of natural disasters such as hurricanes, floods and wildfires. We build on a case study of traffic evacuation planning for the city of Mill Valley, CA. We run a microscopic traffic
Externí odkaz:
http://arxiv.org/abs/2307.07108
Autor:
Boral, Anudhyan, Wan, Zhong Yi, Zepeda-Núñez, Leonardo, Lottes, James, Wang, Qing, Chen, Yi-fan, Anderson, John Roberts, Sha, Fei
We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for stochastic modeling. The ideal LES models the
Externí odkaz:
http://arxiv.org/abs/2306.01174
Autor:
Wan, Zhong Yi, Baptista, Ricardo, Chen, Yi-fan, Anderson, John, Boral, Anudhyan, Sha, Fei, Zepeda-Núñez, Leonardo
We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data. Statistical downscaling seeks a probabilistic map to transform low-resolution data from a biased coarse-grained numerical scheme to high-resolution data
Externí odkaz:
http://arxiv.org/abs/2305.15618
Autor:
Huang, Po-Hsuan, Pan, Yi-Hsiang, Luo, Ying-Sheng, Chen, Yi-Fan, Lo, Yu-Cheng, Chen, Trista Pei-Chun, Perng, Cherng-Kang
This paper presents a deep learning-based wound classification tool that can assist medical personnel in non-wound care specialization to classify five key wound conditions, namely deep wound, infected wound, arterial wound, venous wound, and pressur
Externí odkaz:
http://arxiv.org/abs/2303.16522
Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly underst
Externí odkaz:
http://arxiv.org/abs/2301.04698
Autor:
Wang, Qing, Ihme, Matthias, Linn, Rod R., Chen, Yi-Fan, Yang, Vivian, Sha, Fei, Clements, Craig, McDanold, Jenna S., Anderson, John
As the impact of wildfires has become increasingly more severe over the last decades, there is continued pressure for improvements in our ability to predict wildland fire behavior over a wide range of conditions. One approach towards this goal is thr
Externí odkaz:
http://arxiv.org/abs/2212.05141