HRMSYM: A Fortran subroutine for the numerical computation of a vector integrals over an infinite region with a Gaussian weight function. This is software for an implementation of rules described in the paper ”Fully Symmetric Interpolatory Rules for Multiple Integrals over Infinite Regions with Gaussian Weight”. Also included is software for Gauss-Hermite product rules for a maximum of fifty points in each variable.

References in zbMATH (referenced in 35 articles , 1 standard article )

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  1. Barajas-Solano, David A.; Tartakovsky, Daniel M.: Stochastic collocation methods for nonlinear parabolic equations with random coefficients (2016)
  2. Ko, Jordan; Wynn, Henry P.: The algebraic method in quadrature for uncertainty quantification (2016)
  3. Nobile, Fabio; Tamellini, Lorenzo; Tesei, Francesco; Tempone, Raúl: An adaptive sparse grid algorithm for elliptic PDEs with lognormal diffusion coefficient (2016)
  4. Rahman, Sharif; Ren, Xuchun; Yadav, Vaibhav: High-dimensional stochastic design optimization by adaptive-sparse polynomial dimensional decomposition (2016)
  5. Chen, Michael; Mehrotra, Sanjay; Papp, Dávid: Scenario generation for stochastic optimization problems via the sparse grid method (2015)
  6. Constantine, Paul G.: Active subspaces. Emerging ideas for dimension reduction in parameter studies (2015)
  7. Lei, H.; Yang, X.; Zheng, B.; Lin, G.; Baker, N.A.: Constructing surrogate models of complex systems with enhanced sparsity: quantifying the influence of conformational uncertainty in biomolecular solvation (2015)
  8. Zhang, Zhongqiang; Tretyakov, Michael V.; Rozovskii, Boris; Karniadakis, George E.: Wiener chaos versus stochastic collocation methods for linear advection-diffusion-reaction equations with multiplicative white noise (2015)
  9. Beck, Joakim; Nobile, Fabio; Tamellini, Lorenzo; Tempone, Raúl: A quasi-optimal sparse grids procedure for groundwater flows (2014)
  10. Da Fonseca, José; Grasselli, Martino; Ielpo, Florian: Estimating the Wishart affine stochastic correlation model using the empirical characteristic function (2014)
  11. Geraci, Marco; Bottai, Matteo: Linear quantile mixed models (2014)
  12. Horwood, Joshua T.; Poore, Aubrey B.: Gauss von Mises distribution for improved uncertainty realism in space situational awareness (2014)
  13. Narayan, Akil; Jakeman, John D.: Adaptive Leja sparse grid constructions for stochastic collocation and high-dimensional approximation (2014)
  14. Yadav, Vaibhav; Rahman, Sharif: Adaptive-sparse polynomial dimensional decomposition methods for high-dimensional stochastic computing (2014)
  15. Zhang, Xin-Chun; Teng, Yun-Long: A new derivation of the cubature Kalman filters (2014)
  16. Geletu, Abebe; Klöppel, Michael; Zhang, Hui; Li, Pu: Advances and applications of chance-constrained approaches to systems optimisation under uncertainty (2013)
  17. Ko, Jordan; Garnier, Josselin: Multi-element stochastic spectral projection for high quantile estimation (2013)
  18. Zhang, Zhongqiang; Yang, Xiu; Lin, Guang; Karniadakis, George Em: Numerical solution of the Stratonovich- and Ito-Euler equations: application to the stochastic piston problem (2013)
  19. Xu, Jun; Chen, Jianbing; Li, Jie: Probability density evolution analysis of engineering structures via cubature points (2012)
  20. Holtz, Markus: Sparse grid quadrature in high dimensions with applications in finance and insurance (2011)

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