Uncertainty Quantification for Low-Frequency, Time-Harmonic Maxwell Equations with Stochastic Conductivity Models
Uncertainty Quantification for Low-Frequency, Time-Harmonic Maxwell Equations with Stochastic Conductivity Models is a scholarly work, published in 2018 in ''SIAM/ASA Journal on Uncertainty Quantification''. The main subjects of the publication include Sparse grid, rate of convergence, parametric statistics, regularization, physics-informed neural networks, uncertainty quantification, mathematical optimization, random field, estimator, applied mathematics, mathematics, and solver. The authors develop goal-oriented, primal-dual based, a posteriori error estimators that enable an adaptive, greedy construction of the reduced problem using training sets that are selected from a sparse grid algorithm.