Results 21 to 30 of about 59,334 (307)
Engineering Applications of Computational Fluid Mechanics, 2022 Extremely complex flow channels and multi-parameter, highly nonlinear thermal fluid–structure interactions are the main factors restricting the exploration mechanism and optimal design of explosion-proof valves.
Chaoyong Zong +6 more
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IEEE Access, 2020 This paper presents an innovative modeling strategy for the construction of efficient and compact surrogate models for the uncertainty quantification of time-domain responses of digital links. The proposed approach relies on a two-step methodology. First,
Paolo Manfredi, Riccardo Trinchero
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GTApprox: Surrogate modeling for industrial design [PDF]
Advances in Engineering Software, 2016 31 pages, 11 ...
Mikhail Belyaev +6 more
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Active learning to understand infectious disease models and improve policy making. [PDF]
PLoS Computational Biology, 2014 Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding.
Lander Willem +5 more
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Machine-learning-based surrogate modeling of aerodynamic flow around distributed structures [PDF]
, 2021 A machine-learning-based surrogate modeling method for distributed fluid systems is proposed in this paper, where a dimensionality reduction technique is used to reduce the flowfield dimension and a regression model is used to predict the reduced ...
Zhao, Xiaowei, Zhang, Jincheng
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Multifidelity Surrogate Models for Efficient Uncertainty Propagation Analysis in Salars Systems
Frontiers in Water, 2022 Salars are complex hydrogeological systems where the high-density contrasts require advanced numerical models to simulate groundwater flow and brine transport.
Vasileios Christelis, Andrew G. Hughes
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On the use of gradients in Kriging surrogate models [PDF]
Proceedings of the Winter Simulation Conference 2014, 2014 The use of Kriging surrogate models has become popular in approximating computation-intensive deterministic computer models. In this work, the effect of enhancing Kriging surrogate models with a (partial) set of gradients is investigated. While, intuitively, gradient information is useful to enhance prediction accuracy, another motivation behind this ...
Selvakumar Ulaganathan +4 more
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Universal Prediction Distribution for Surrogate Models [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2017 The use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions.
Ben Salem, Malek +3 more
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Multi-fidelity reduced-order surrogate modeling [PDF]
, 2023 High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated for modeling a given system. Multi-
Conti, Paolo +5 more
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Surrogate modeling of RF circuit blocks [PDF]
, 2010 Surrogate models are a cost-effective replacement for expensive computer simulations in design space exploration. Literature has already demonstrated the feasibility of accurate surrogate models for single radio frequency (RF) and microwave devices ...
Tom Dhaene +7 more
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