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pro vyhledávání: '"Ye, Dongwei"'
Autor:
Kevopoulos, Konstantinos, Ye, Dongwei
Surrogate modelling is widely applied in computational science and engineering to mitigate computational efficiency issues for the real-time simulations of complex and large-scale computational models or for many-query scenarios, such as uncertainty
Externí odkaz:
http://arxiv.org/abs/2409.16817
Autor:
Ye, Dongwei, Guo, Mengwu
One of the pivotal tasks in scientific machine learning is to represent underlying dynamical systems from time series data. Many methods for such dynamics learning explicitly require the derivatives of state data, which are not directly available and
Externí odkaz:
http://arxiv.org/abs/2312.12193
Autor:
Ye, Dongwei, Guo, Mengwu
Conventional Gaussian process regression exclusively assumes the existence of noise in the output data of model observations. In many scientific and engineering applications, however, the input locations of observational data may also be compromised
Externí odkaz:
http://arxiv.org/abs/2305.11586
Parametric reduced-order modelling often serves as a surrogate method for hemodynamics simulations to improve the computational efficiency in many-query scenarios or to perform real-time simulations. However, the snapshots of the method require to be
Externí odkaz:
http://arxiv.org/abs/2302.11006
Autor:
Ye, Dongwei, Guo, Mengwu
Publikováno v:
In Communications in Nonlinear Science and Numerical Simulation November 2024 138
Disorders of coronary arteries lead to severe health problems such as atherosclerosis, angina, heart attack and even death. Considering the clinical significance of coronary arteries, an efficient computational model is a vital step towards tissue en
Externí odkaz:
http://arxiv.org/abs/2204.02167
Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling
In-Stent Restenosis is a recurrence of coronary artery narrowing due to vascular injury caused by balloon dilation and stent placement. It may lead to the relapse of angina symptoms or to an acute coronary syndrome. An uncertainty quantification of a
Externí odkaz:
http://arxiv.org/abs/2111.06173
Uncertainty estimations are presented of the response of a multiscale in-stent restenosis model, as obtained by both non-intrusive and semi-intrusive uncertainty quantification. The in-stent restenosis model is a fully coupled multiscale simulation o
Externí odkaz:
http://arxiv.org/abs/2009.00354
Publikováno v:
In Journal of Computational Physics 15 January 2024 497
Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modelling
Publikováno v:
In Reliability Engineering and System Safety October 2023 238