ATHENA: Advanced Techniques for High Dimensional Parameter Spaces to Enhance Numerical Analysis

Abstract

ATHENA is an open source Python package for reduction in parameter space. It implements several advanced numerical analysis techniques such as Active Subspaces (AS), Kernel-based Active Subspaces (KAS), and Nonlinear Level-set Learning (NLL) method. It is intended as a tool for regression, sensitivity analysis, and in general to enhance existing numerical simulations' pipelines tackling the curse of dimensionality. Source code, documentation, and several tutorials are available on GitHub at https://github.com/mathLab/ATHENA under the MIT license.

References in corpus (2)

• Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
• A local approach to parameter space reduction for regression and classification tasks

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