Hull-form stochastic optimization via computational-cost reduction methods
The paper shows how cost-reduction methods can be synergistically combined to enable
high-fidelity hull-form optimization under stochastic conditions. Specifically, a multi-objective …
high-fidelity hull-form optimization under stochastic conditions. Specifically, a multi-objective …
Comparison of multi-fidelity approaches for military vehicle design
This paper overviews the efforts of a technical team within the NATO Applied Vehicle
Technology Panel to apply multi-fidelity methods to vehicle design. The objectives of the …
Technology Panel to apply multi-fidelity methods to vehicle design. The objectives of the …
Multi-fidelity model and reduced-order method for comprehensive hydrodynamic performance optimization and prediction of JBC ship
Abstract The multi-fidelity Co-Kriging surrogate model can be applied to combine the
accuracy advantage of high-fidelity sample evaluation with the efficiency advantage of low …
accuracy advantage of high-fidelity sample evaluation with the efficiency advantage of low …
Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance
This paper presents a comparison of two multi-fidelity methods for the forward uncertainty
quantification of a naval engineering problem. Specifically, we consider the problem of …
quantification of a naval engineering problem. Specifically, we consider the problem of …
A multi-fidelity polynomial chaos-greedy Kaczmarz approach for resource-efficient uncertainty quantification on limited budget
Polynomial chaos expansion (PCE) has been widely used to facilitate uncertainty
quantification and stochastic computations for complex systems. Multi-fidelity approaches …
quantification and stochastic computations for complex systems. Multi-fidelity approaches …
Control variate polynomial chaos: Optimal fusion of sampling and surrogates for multifidelity uncertainty quantification
We present a multifidelity uncertainty quantification numerical method that leverages the
benefits of both sampling and surrogate modeling, while mitigating their downsides, for …
benefits of both sampling and surrogate modeling, while mitigating their downsides, for …
Multi-Fidelity Low-Rank Approximations for Uncertainty Quantification of a Supersonic Aircraft Design
Uncertainty quantification has proven to be an indispensable study for enhancing reliability
and robustness of engineering systems in the early design phase. Single and multi-fidelity …
and robustness of engineering systems in the early design phase. Single and multi-fidelity …
Lp CONVERGENCE OF APPROXIMATE MAPS AND PROBABILITY DENSITIES FOR FORWARD AND INVERSE PROBLEMS IN UNCERTAINTY QUANTIFICATION
This work analyzes the convergence of probability densities solving uncertainty
quantification problems for computational models where the mapping between input and …
quantification problems for computational models where the mapping between input and …
Design of Experiments via Multi-Fidelity Surrogates and Statistical Sensitivity Measures
Parameter estimation and optimal experimental design problems have been widely studied
across science and engineering. The two are inextricably linked, with optimally designed …
across science and engineering. The two are inextricably linked, with optimally designed …
Spectral convergence of probability densities for forward problems in uncertainty quantification
A Sagiv - Numerische Mathematik, 2022 - Springer
The estimation of probability density functions (PDF) using approximate maps is a
fundamental building block in computational probability. We consider forward problems in …
fundamental building block in computational probability. We consider forward problems in …