Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs
<|reference_start|>Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs: This work considers the computation of risk measures for quantities of interest governed by PDEs with Gaussian random field parameters using Taylor approximations. While efficient, Taylor approximations are local to the point of expansion, and hence may degrade in accuracy when the variances of the input parameters are large. To address this challenge, we approximate the underlying Gaussian measure by a mixture of Gaussians with reduced variance in a dominant direction of parameter space. Taylor approximations are constructed at the means of each Gaussian mixture component, which are then combined to approximate the risk measures. The formulation is presented in the setting of infinite-dimensional Gaussian random parameters for risk measures including the mean, variance, and conditional value-at-risk. We also provide detailed analysis of the approximations errors arising from two sources: the Gaussian mixture approximation and the Taylor approximations. Numerical experiments are conducted for a semilinear advection-diffusion-reaction equation with a random diffusion coefficient field and for the Helmholtz equation with a random wave speed field. For these examples, the proposed approximation strategy can achieve less than $1\%$ relative error in estimating CVaR with only $\mathcal{O}(10)$ state PDE solves, which is comparable to a standard Monte Carlo estimate with $\mathcal{O}(10^4)$ samples, thus achieving significant reduction in computational cost. The proposed method can therefore serve as a way to rapidly and accurately estimate risk measures under limited computational budgets.<|reference_end|>
@article{luo2024gaussian,
    title={Gaussian mixture Taylor approximations of risk measures constrained by
  PDEs with Gaussian random field inputs},
    author={Dingcheng Luo, Joshua Chen, Peng Chen, Omar Ghattas},
    journal={arXiv preprint arXiv:2408.06615},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06615},
    primaryClass={math.NA cs.NA stat.CO}
}