Towards a machine learning pipeline in reduced order modelling for
inverse problems: neural networks for boundary parametrization,
dimensionality reduction and solution manifold approximation
- URL: http://arxiv.org/abs/2210.14764v1
- Date: Wed, 26 Oct 2022 14:53:07 GMT
- Title: Towards a machine learning pipeline in reduced order modelling for
inverse problems: neural networks for boundary parametrization,
dimensionality reduction and solution manifold approximation
- Authors: Anna Ivagnes, Nicola Demo, Gianluigi Rozza
- Abstract summary: Inverse problems, especially in a partial differential equation context, require a huge computational load.
We apply a numerical pipeline that involves artificial neural networks to parametrize the boundary conditions of the problem in hand.
It derives a general framework capable to provide an ad-hoc parametrization of the inlet boundary and quickly converges to the optimal solution.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we propose a model order reduction framework to deal with
inverse problems in a non-intrusive setting. Inverse problems, especially in a
partial differential equation context, require a huge computational load due to
the iterative optimization process. To accelerate such a procedure, we apply a
numerical pipeline that involves artificial neural networks to parametrize the
boundary conditions of the problem in hand, compress the dimensionality of the
(full-order) snapshots, and approximate the parametric solution manifold. It
derives a general framework capable to provide an ad-hoc parametrization of the
inlet boundary and quickly converges to the optimal solution thanks to model
order reduction. We present in this contribution the results obtained by
applying such methods to two different CFD test cases.
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