/polynomial_surrogates

Tools to construct surrogate models based on Hermitian polynomial bases. Includes full-factorial and sparse polynomial chaos expansions via least-angle regression as well as continuous-space low-rank approximations in canonical polyadics format.

Primary LanguagePython

This is an implemenatation of three polynomial basis surrogate models, namely:

  • full polynomial chaos expansions

  • sparse polynomials chaos expansions according to

Blatman, G. and B. Sudret (2011). Adaptive sparse polynomial chaos expansion based on least-angle regression. Journal of Computational Physics 230(6), 2345 - 2367.

  • continuous-space low-rank approximations in canonical polyadics format according to

Konakli, K. and B. Sudret (2016b). Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions. Journal of Computational Physics 321, 1144 - 1169.

Includes Variance-based sensitivity analysis for

  • full and sparse PCE-based according to

Sudret, B. (2008). Global sensitivity analysis using polynomial chaos expansions. Reliability Engineering And System Safety 93(7), 964 - 979.

  • (not yet) LRA-based variance-based sensitivity indices according to

Konakli, K. and B. Sudret (2016). Global sensitivity analysis using low-rank tensor approximations. Reliability Engineering & System Safety 156 (Supplement C), 64 - 83.