HAL - Hierarchical Active Learning

Python module for active learning in hierarchical (Bayesian network type of) structural reliability models.

Code based on the paper C. Agrell and K. R. Dahl (2021) Sequential Bayesian optimal experimental design for structural reliability analysis.

HAL contains classes for defining SRA models with epistemic uncertainty, including GP emulators, making use of the following submodules:

HAL/SRA: For Structural Reliability Analysis (SRA) with Monte Carlo simulation, importance sampling, design point search and FORM (First Order Reliability Method)

HAL/GP: Gaussian Process (GP) module with fast evaluation of posterior when a single observation is added at some specified new input location x_new

Prerequisites

The code has been tested with the following requirements:

Python 3 (3.9.1 64bit)

• numpy (1.19.5)
• scipy (1.6.0)
• sklearn (0.24.1) Only uses the function sklearn.metrics.pairwise.euclidean_distances from this package for fast computation of Gram matrices (and could easily be replaced by custom code if needed)
• filterpy (1.4.5) Currently using the UT implementation from filterpy for UT calculations (unscented_transform and MerweScaledSigmaPoints from filterpy.kalman). Might replace this later with a custom UT package for other sigma-point selection methods

Examples

Some examples are given in jupyter notebooks.