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08/07/2023
Generative Models for the Deformation of Industrial Shapes with Linear Geometric Constraints: model order and parameter space reductions
Real-world applications of computational fluid dynamics often involve th...
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08/07/2023
Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations
A slow decaying Kolmogorov n-width of the solution manifold of a paramet...
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08/07/2023
Friedrichs' systems discretized with the Discontinuous Galerkin method: domain decomposable model order reduction and Graph Neural Networks approximating vanishing viscosity so
Friedrichs' systems (FS) are symmetric positive linear systems of first-...
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03/01/2022
Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Non-affine parametric dependencies, nonlinearities and advection-dominat...
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10/27/2021
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Multi-fidelity models are of great importance due to their capability of...
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07/22/2021
A local approach to parameter space reduction for regression and classification tasks
Frequently, the parameter space, chosen for shape design or other applic...
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05/14/2021
ATHENA: Advanced Techniques for High Dimensional Parameter Spaces to Enhance Numerical Analysis
ATHENA is an open source Python package for reduction in parameter space...
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10/16/2020
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
Gaussian processes are employed for non-parametric regression in a Bayes...
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08/27/2020