Data-driven modeling for unsteady aerodynamics and aeroelasticity
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …
addition to experiment and numerical simulation, due to its low-dimensional representation …
Surrogate-assisted global sensitivity analysis: an overview
K Cheng, Z Lu, C Ling, S Zhou - Structural and Multidisciplinary …, 2020 - Springer
Surrogate models are popular tool to approximate the functional relationship of expensive
simulation models in multiple scientific and engineering disciplines. Successful use of …
simulation models in multiple scientific and engineering disciplines. Successful use of …
[HTML][HTML] Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe
The European potential for renewable electricity is sufficient to enable fully renewable
supply on different scales, from self-sufficient, subnational regions to an interconnected …
supply on different scales, from self-sufficient, subnational regions to an interconnected …
Transfer learning based multi-fidelity physics informed deep neural network
S Chakraborty - Journal of Computational Physics, 2021 - Elsevier
For many systems in science and engineering, the governing differential equation is either
not known or known in an approximate sense. Analyses and design of such systems are …
not known or known in an approximate sense. Analyses and design of such systems are …
Review of polynomial chaos-based methods for uncertainty quantification in modern integrated circuits
Advances in manufacturing process technology are key ensembles for the production of
integrated circuits in the sub-micrometer region. It is of paramount importance to assess the …
integrated circuits in the sub-micrometer region. It is of paramount importance to assess the …
Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
The polynomial chaos (PC) expansion has been widely used as a surrogate model in the
Bayesian inference to speed up the Markov chain Monte Carlo (MCMC) calculations …
Bayesian inference to speed up the Markov chain Monte Carlo (MCMC) calculations …
[HTML][HTML] A probabilistic framework for source localization in anisotropic composite using transfer learning based multi-fidelity physics informed neural network (mfPINN …
The practical application of data-driven frameworks like deep neural network in acoustic
emission (AE) source localization is impeded due to the collection of significant clean data …
emission (AE) source localization is impeded due to the collection of significant clean data …
[PDF][PDF] 变可信度近似模型及其在复杂装备优化设计中的应用研究进展
周奇， 杨扬， 宋学官， 韩忠华， 程远胜， 胡杰翔… - 机械工程 …, 2020 - scholar.archive.org
变可信度近似模型通过融合不同精度分析模型的数据, 可有效平衡近似模型预测性能和建模成本
之间的矛盾, 在复杂装备优化设计中受到广泛的关注. 综述变可信度近似模型及其在复杂装备 …
之间的矛盾, 在复杂装备优化设计中受到广泛的关注. 综述变可信度近似模型及其在复杂装备 …
[HTML][HTML] Multi-fidelity modeling framework for nonlinear unsteady aerodynamics of airfoils
Aerodynamic data can be obtained from different sources, which vary in fidelity, availability
and cost. As the fidelity of data increases, the cost of data acquisition usually becomes …
and cost. As the fidelity of data increases, the cost of data acquisition usually becomes …
Global sensitivity analysis via multi-fidelity polynomial chaos expansion
The presence of uncertainties is inevitable in engineering design and analysis, where failure
in understanding their effects might lead to the structural or functional failure of the systems …
in understanding their effects might lead to the structural or functional failure of the systems …