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08/03/2023
An optimisation-based domain-decomposition reduced order model for parameter-dependent non-stationary fluid dynamics problems
In this work, we address parametric non-stationary fluid dynamics proble...
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01/14/2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that e...
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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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08/30/2021
Thermomechanical modelling for industrial applications
In this work we briefly present a thermomechanical model that could serv...
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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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02/16/2021
Fluid-structure interaction simulations with a LES filtering approach in solids4Foam
We consider the interaction of an incompressible fluid described by a Le...
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08/27/2020
Kernel-based Active Subspaces with application to CFD parametric problems using Discontinuous Galerkin method
A new method to perform a nonlinear reduction in parameter spaces is pro...
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07/16/2019
