Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis
L Cao, J Liu, C Jiang, G Liu - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
Polynomial chaos expansion has received considerable attention in uncertainty
quantification since its great modeling capability for complex systems. However, considering …
quantification since its great modeling capability for complex systems. However, considering …
[HTML][HTML] A spectral surrogate model for stochastic simulators computed from trajectory samples
Stochastic simulators are non-deterministic computer models which provide a different
response each time they are run, even when the input parameters are held at fixed values …
response each time they are run, even when the input parameters are held at fixed values …
[PDF][PDF] Whole-energy system models: The advisors for the energy transition
Climate change is making the transition to more renewable and sustainable energy systems
an urgent global priority. Countries and communities around the world are developing their …
an urgent global priority. Countries and communities around the world are developing their …
Efficient uncertainty quantification of stochastic problems in CFD by combination of compressed sensing and POD-Kriging
Q Lu, L Wang, L Li - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
This paper proposes an uncertainty quantification method that combines compressed
sensing and POD-Kriging that inherits the benefits of each key element. The compressed …
sensing and POD-Kriging that inherits the benefits of each key element. The compressed …
[HTML][HTML] Global sensitivity analysis for multivariate outputs using polynomial chaos-based surrogate models
We propose an efficient global sensitivity analysis method for multivariate outputs that
applies polynomial chaos-based surrogate models to vector projection-based sensitivity …
applies polynomial chaos-based surrogate models to vector projection-based sensitivity …
Efficient uncertainty quantification of CFD problems by combination of proper orthogonal decomposition and compressed sensing
In the current paper, an efficient surrogate model based on combination of Proper
Orthogonal Decomposition (POD) and compressed sensing is developed for affordable …
Orthogonal Decomposition (POD) and compressed sensing is developed for affordable …
A novel surrogate for extremes of random functions
Numerical solutions of stochastic problems require the representation of random functions in
their definitions by finite dimensional (FD) models, ie, deterministic functions of time and …
their definitions by finite dimensional (FD) models, ie, deterministic functions of time and …
Uncertainty Propagation in High-Dimensional Fields using Non-Intrusive Reduced Order Modeling and Polynomial Chaos
View Video Presentation: https://doi. org/10.2514/6.2023-1686. vid High-fidelity, physics-
based modeling and simulation have become integral to the design of aircraft, but can have …
based modeling and simulation have become integral to the design of aircraft, but can have …
Robust operational optimization of a typical micro gas turbine
Due to their high total efficiency and flexibility, micro Gas Turbines (mGTs) offer great
potential for use in small-scale distributed cogeneration applications. The economic success …
potential for use in small-scale distributed cogeneration applications. The economic success …
Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion
We propose a non-intrusive reduced-order modeling method based on proper orthogonal
decomposition (POD) and polynomial chaos expansion (PCE) for stochastic representations …
decomposition (POD) and polynomial chaos expansion (PCE) for stochastic representations …