Representation of Typical Droplet Shapes in 2D Latent Space
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916966659260416 |
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| author | Durve, Mihir Montessori, Jean-Michel Tucny Andrea Lauricella, Marco Succi, Sauro |
| author_facet | Durve, Mihir Montessori, Jean-Michel Tucny Andrea Lauricella, Marco Succi, Sauro |
| contents | In this study, we investigate the clustering of 5000 droplets, each originating from one of five distinct droplet classes, each representing a unique geometry. The shape coordinates of the droplets are mapped to a 2D latent space through a two-step, fully reversible process involving Fourier series and autoencoders. Thus, each droplet contour sampled by 400 points is represented by 2 scalar numbers. We present data mapping 5000 droplets in two-dimensional space, which reveals ringlike clusters. Each cluster corresponds to a distinct droplet class, with the classification based on the droplet morphology, which is known a priori. Further, we find that well-known clustering algorithms like DBSCAN show limited success in identifying these ring-shaped clusters underscoring the need to explore alternative clustering approaches, particularly those tailored to handle ring-shaped distributed data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Representation of Typical Droplet Shapes in 2D Latent Space Durve, Mihir Montessori, Jean-Michel Tucny Andrea Lauricella, Marco Succi, Sauro Fluid Dynamics In this study, we investigate the clustering of 5000 droplets, each originating from one of five distinct droplet classes, each representing a unique geometry. The shape coordinates of the droplets are mapped to a 2D latent space through a two-step, fully reversible process involving Fourier series and autoencoders. Thus, each droplet contour sampled by 400 points is represented by 2 scalar numbers. We present data mapping 5000 droplets in two-dimensional space, which reveals ringlike clusters. Each cluster corresponds to a distinct droplet class, with the classification based on the droplet morphology, which is known a priori. Further, we find that well-known clustering algorithms like DBSCAN show limited success in identifying these ring-shaped clusters underscoring the need to explore alternative clustering approaches, particularly those tailored to handle ring-shaped distributed data. |
| title | Representation of Typical Droplet Shapes in 2D Latent Space |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2509.19493 |