Non-Linear Orthogonal Modal Decompositions in Turbulent Flows via Autoencoders

QC 20230621
We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of nonlinear modes from high-fidelity turbulent-flow data. Our approach is based on β-variational autoencoders (β-VAEs) and convolutional neural networks (CNNs), which enable extracting non-linear modes from multi-scale turbulent flows while encouraging the learning of independent latent variables and penalizing the size of the latent vector. Moreover, we introduce an algorithm for ordering VAE-based modes with respect to their contribution to the reconstruction. We apply this method for non-linear mode decomposition of the turbulent flow through a simplified urban environment. We demonstrate that by constraining the shape of the latent space, it is possible to motivate the orthogonality and extract a set of parsimonious modes sufficient for high-quality reconstruction. Our results show the excellent performance of the method in the reconstruction against linear-theory-based decompositions. We show the ability of our approach in the extraction of near-orthogonal modes with the determinant of the correlation matrix equal to 0.99, which may lead to interpretability.

Le Clainche, SoledadSchool of Aerospace Engineering Universidad Politécnica de Madrid Madrid, Spain (författare)
Hoyas, SergioInst. Univ. Matemática Pura y Aplicada Universitat Politècnica de València Valencia, Spain (författare)
Vinuesa, RicardoKTH,Linné Flow Center, FLOW,Teknisk mekanik(Swepub:kth)u12xffui (författare)
KTHLinné Flow Center, FLOW (creator_code:org_t)

Ingår i:12th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2022: International Symposium on Turbulence and Shear Flow Phenomena, TSFP

Av författaren/redakt...: Eivazi, Hamidrez ...; Le Clainche, Sol ...; Hoyas, Sergio; Vinuesa, Ricardo

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Maskinteknik; och Strömningsmekani ...

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