A deep learning‒genetic algorithm approach for aerodynamic inverse design via optimization of pressure distribution
Conventional aerodynamic inverse design (AID) methods have major limitations in terms of
optimality and actuality of target parameter distribution. In this research, the target pressure …
optimality and actuality of target parameter distribution. In this research, the target pressure …
Kriging metamodel-based seismic fragility analysis of single-bent reinforced concrete highway bridges
Uncertainty quantification is an important issue in the seismic fragility analysis of bridge type
structures. However, the influence of different sources of uncertainty on the seismic fragility …
structures. However, the influence of different sources of uncertainty on the seismic fragility …
Multi-fidelity surrogate model ensemble based on feasible intervals
Multi-fidelity surrogate models received a lot of attention in engineering optimization due to
their ability to achieve the required accuracy at a lower cost. However, selecting an …
their ability to achieve the required accuracy at a lower cost. However, selecting an …
A derivative-free line-search algorithm for simulation-driven design optimization using multi-fidelity computations
The paper presents a multi-fidelity extension of a local line-search-based derivative-free
algorithm for nonsmooth constrained optimization (MF-CS-DFN). The method is intended for …
algorithm for nonsmooth constrained optimization (MF-CS-DFN). The method is intended for …
[PDF][PDF] Kriging Metamodel-Based Seismic Fragility Analysis of Single-Bent Reinforced Concrete Highway Bridges. Buildings 2021, 11, 238
Uncertainty quantification is an important issue in the seismic fragility analysis of bridge type
structures. However, the influence of different sources of uncertainty on the seismic fragility …
structures. However, the influence of different sources of uncertainty on the seismic fragility …