mlsanityData to 7 Oct 2026

A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems

arXiv 2301.09926 ↗First posted 24 Jan 2023math.NA cs.LG cs.NAcited by 1

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Abstract (arXiv)

Parametric time-dependent systems are of a crucial importance in modeling real phenomena, often characterized by non-linear behaviors too. Those solutions are typically difficult to generalize in a sufficiently wide parameter space while counting on limited computational resources available. As such, we present a general two-stages deep learning framework able to perform that generalization with low computational effort in time. It consists in a separated training of two pipe-lined predictive models. At first, a certain number of independent neural networks are trained with data-sets taken from different subsets of the parameter space. Successively, a second predictive model is specialized to properly combine the first-stage guesses and compute the right predictions. Promising results are obtained applying the framework to incompressible Navier-Stokes equations in a cavity (Rayleigh-Bernard cavity), obtaining a 97% reduction in the computational time comparing with its numerical resolution for a new value of the Grashof number.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 58 references that are in the map. First posted on arXiv 24 Jan 2023.

Builds on

13 of 58 references.

References · 13

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1 more
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Generative Adversarial Reduced Order Modellingcited by 7