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.

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Cited by in corpus (8)

• A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls
• A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems
• Enhancing CFD predictions in shape design problems by model and parameter space reduction
• A Dimensionality Reduction Approach for Convolutional Neural Networks
• Kernel-based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method
• Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
• A local approach to parameter space reduction for regression and classification tasks
• Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces