Zobrazeno 1 - 10
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pro vyhledávání: '"Zhao Hongli"'
We propose a data-driven approach for propagating uncertainty in stochastic power grid simulations and apply it to the estimation of transmission line failure probabilities. A reduced-order equation governing the evolution of the observed line energy
Externí odkaz:
http://arxiv.org/abs/2401.02555
We introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colored) noise. Unlike systems subjected to Gaussian white noise, a determ
Externí odkaz:
http://arxiv.org/abs/2312.10243
Autor:
Zhao, Hongli, Tartakovsky, Daniel M.
Discovery of mathematical descriptors of physical phenomena from observational and simulated data, as opposed to from the first principles, is a rapidly evolving research area. Two factors, time-dependence of the inputs and hidden translation invaria
Externí odkaz:
http://arxiv.org/abs/2309.05117
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples. Normalizing flows have
Externí odkaz:
http://arxiv.org/abs/2305.02460
Publikováno v:
MATEC Web of Conferences, Vol 232, p 04052 (2018)
Aiming at aerospace remote sensing technology, this paper proposes a dualsatellite integrated intelligent reconnaissance decision-making model, and establishes a doublesatellite system. One satellite is used for “general investigation” and one sa
Externí odkaz:
https://doaj.org/article/9b3bc388178f497bac04b5ca1cc6ffa9
Publikováno v:
MATEC Web of Conferences, Vol 246, p 02006 (2018)
Irrigation of agricultural land is the main water consumer in the arid and semiarid regions. The accurate time series of daily evapotranspiration (ET) at the field scale is crucial for irrigation water management. Here, we presented an integrated app
Externí odkaz:
https://doaj.org/article/de814c808a9c4eb19289c88c57c0ab10
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data. The method is based on tensor-train and flow-based generative modeling. Our method first efficiently constructs an approximat
Externí odkaz:
http://arxiv.org/abs/2212.00759
Autor:
Zhao, Hongli, Qin, Fangcheng, Jiang, Jixun, Cui, Yuxin, Wang, Hengxing, Kang, Yuehua, Zhang, Lianming
Publikováno v:
In Materials Today Communications December 2024 41
Publikováno v:
In Applied Soft Computing December 2024 167 Part B
Publikováno v:
In Journal of Chromatography A 8 November 2024 1736