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An Efficient Non-Intrusive Uncertainty Propagation Method for Stochastic Multi-Physics Models
Akshay Mittal, Gianluca Iaccarino
Multi-physics models governed by coupled partial differential equation (PDE) systems, are naturally suited for partitioned, or modular numerical solution strategies. Although widel…
A Flexible Uncertainty Quantification Framework for General Multi-Physics Systems
Akshay Mittal, Xiao Chen, Charles Tong +1
We present a "module-based hybrid" Uncertainty Quantification (UQ) framework for general nonlinear multi-physics simulation. The proposed methodology, introduced in [\hyperlink{ref…
An Efficient Intrusive Uncertainty Propagation Method For Multi-Physics System With Random Inputs
Akshay Mittal, Gianluca Iaccarino
Coupled partial differential equation (PDE) systems, which often represent multi-physics models, are naturally suited for modular numerical solution methods. However, several chall…
A density-matching approach for optimization under uncertainty
Pranay Seshadri, Paul Constantine, Gianluca Iaccarino +1
Modern computers enable methods for design optimization that account for uncertainty in the system---so-called optimization under uncertainty. We propose a metric for OUU that meas…
Exploiting Active Subspaces to Quantify Uncertainty in the Numerical Simulation of the HyShot II Scramjet
Paul Constantine, Michael Emory, Johan Larsson +1
We present a computational analysis of the reactive flow in a hypersonic scramjet engine with focus on effects of uncertainties in the operating conditions. We employ a novel metho…
Optimization Under Uncertainty Using the Generalized Inverse Distribution Function
Domenico Quagliarella, Giovanni Petrone, Gianluca Iaccarino
A framework for robust optimization under uncertainty based on the use of the generalized inverse distribution function (GIDF), also called quantile function, is here proposed. Com…