Work

PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling

Year: 2023

Type: article

Source: Environmental Modelling & Software

DOI: 10.1016/j.envsoft.2023.105825

Author: J.D. Jakeman

Institution: Sandia National Laboratories

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funders: U.S. Department of Energy, Defense Advanced Research Projects Agency, National Nuclear Security Administration, Advanced Research Projects Agency, Advanced Scientific Computing Research, Biological and Environmental Research, Laboratory Directed Research and Development, Sandia National Laboratories

Awards: G11630931, G1166951225, G1859920734, G2214935549, G336692854, G413247060, G4176796873, G4903105778, G5339743583, G70797072, G855168022

OpenAlex ID: W4387311458

FWCI: 3.55

Cites: 121

Cited by: 19

Related to: 10