Two-dimensional mesh generator in generalized coordinates implemented in
Python
- URL: http://arxiv.org/abs/2110.12875v1
- Date: Sun, 17 Oct 2021 20:42:18 GMT
- Title: Two-dimensional mesh generator in generalized coordinates implemented in
Python
- Authors: Gustavo Taiji Naozuka, Saulo Martiello Mastelini, Eliandro Rodrigues
Cirilo, Neyva Maria Lopes Romeiro and Paulo Laerte Natti
- Abstract summary: The paper presents a two-dimensional mesh generator in generalized coordinates, which uses the Parametric Linear Spline method and partial differential equations.
The generator is automated and able to treat real complex domains.
- Score: 1.1744028458220428
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Through mathematical models, it is possible to turn a problem of the physical
domain into the computational domain. In this context, the paper presents a
two-dimensional mesh generator in generalized coordinates, which uses the
Parametric Linear Spline method and partial differential equations. The
generator is automated and able to treat real complex domains. The code was
implemented in Python, applying the Numpy and Matplotlib libraries to matrix
manipulations and graphical plots, respectively. Applications are made for
monoblock meshes (two-dimensional shape of a bottle) and multi-block meshes
(geometry of Igap\'o I lake, Londrina, Paran\'a, Brazil).
Related papers
- TopoX: A Suite of Python Packages for Machine Learning on Topological
Domains [89.9320422266332]
TopoX is a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains.
TopoX consists of three packages: TopoNetX, TopoEmbedX and TopoModelx.
arXiv Detail & Related papers (2024-02-04T10:41:40Z) - Polygon Detection for Room Layout Estimation using Heterogeneous Graphs
and Wireframes [2.76240219662896]
This paper presents a network method that can be used to solve room layout estimations tasks.
The network takes an RGB image and estimates a wireframe as well as space using an hourglass backbone.
arXiv Detail & Related papers (2023-06-21T11:55:15Z) - Mesh Convolution with Continuous Filters for 3D Surface Parsing [101.25796935464648]
We propose a series of modular operations for effective geometric feature learning from 3D triangle meshes.
Our mesh convolutions exploit spherical harmonics as orthonormal bases to create continuous convolutional filters.
We further contribute a novel hierarchical neural network for perceptual parsing of 3D surfaces, named PicassoNet++.
arXiv Detail & Related papers (2021-12-03T09:16:49Z) - Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via
GDPA Linearization [59.87663954467815]
Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer.
In this paper, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we unroll a projection-free algorithm for semi-definite programming relaxation (SDR) of a binary graph.
Experimental results show that our unrolled network outperformed pure model-based graph classifiers, and achieved comparable performance to pure data-driven networks but using far fewer parameters.
arXiv Detail & Related papers (2021-09-10T07:01:15Z) - Shape Modeling with Spline Partitions [3.222802562733787]
We propose a novel parallelized Bayesian nonparametric approach to partition a domain with curves, enabling complex data-shapes to be acquired.
We apply our method to HIV-1-infected human macrophage image dataset, and also simulated datasets sets to illustrate our approach.
arXiv Detail & Related papers (2021-08-05T10:33:05Z) - Full interpretable machine learning in 2D with inline coordinates [9.13755431537592]
It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space.
It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D.
The classification and regression algorithms based on these inline coordinates were introduced.
arXiv Detail & Related papers (2021-06-14T16:21:06Z) - Primal-Dual Mesh Convolutional Neural Networks [62.165239866312334]
We propose a primal-dual framework drawn from the graph-neural-network literature to triangle meshes.
Our method takes features for both edges and faces of a 3D mesh as input and dynamically aggregates them.
We provide theoretical insights of our approach using tools from the mesh-simplification literature.
arXiv Detail & Related papers (2020-10-23T14:49:02Z) - On Path Integration of Grid Cells: Group Representation and Isotropic
Scaling [135.0473739504851]
We conduct theoretical analysis of a general representation model of path integration by grid cells.
We learn hexagon grid patterns that share similar properties of the grid cells in the rodent brain.
The learned model is capable of accurate long distance path integration.
arXiv Detail & Related papers (2020-06-18T03:44:35Z) - Geomstats: A Python Package for Riemannian Geometry in Machine Learning [5.449970675406181]
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear equations.
We provide object-oriented and extensively unit-tested implementations.
We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics.
The source code is freely available under the MIT license at urlgeomstats.ai.
arXiv Detail & Related papers (2020-04-07T20:41:50Z) - PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling [103.09504572409449]
We propose a novel deep neural network based method, called PUGeo-Net, to generate uniform dense point clouds.
Thanks to its geometry-centric nature, PUGeo-Net works well for both CAD models with sharp features and scanned models with rich geometric details.
arXiv Detail & Related papers (2020-02-24T14:13:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.