804 research outputs found
Finding Hexahedrizations for Small Quadrangulations of the Sphere
This paper tackles the challenging problem of constrained hexahedral meshing.
An algorithm is introduced to build combinatorial hexahedral meshes whose
boundary facets exactly match a given quadrangulation of the topological
sphere. This algorithm is the first practical solution to the problem. It is
able to compute small hexahedral meshes of quadrangulations for which the
previously known best solutions could only be built by hand or contained
thousands of hexahedra. These challenging quadrangulations include the
boundaries of transition templates that are critical for the success of general
hexahedral meshing algorithms.
The algorithm proposed in this paper is dedicated to building combinatorial
hexahedral meshes of small quadrangulations and ignores the geometrical
problem. The key idea of the method is to exploit the equivalence between quad
flips in the boundary and the insertion of hexahedra glued to this boundary.
The tree of all sequences of flipping operations is explored, searching for a
path that transforms the input quadrangulation Q into a new quadrangulation for
which a hexahedral mesh is known. When a small hexahedral mesh exists, a
sequence transforming Q into the boundary of a cube is found; otherwise, a set
of pre-computed hexahedral meshes is used.
A novel approach to deal with the large number of problem symmetries is
proposed. Combined with an efficient backtracking search, it allows small
shellable hexahedral meshes to be found for all even quadrangulations with up
to 20 quadrangles. All 54,943 such quadrangulations were meshed using no more
than 72 hexahedra. This algorithm is also used to find a construction to fill
arbitrary domains, thereby proving that any ball-shaped domain bounded by n
quadrangles can be meshed with no more than 78 n hexahedra. This very
significantly lowers the previous upper bound of 5396 n.Comment: Accepted for SIGGRAPH 201
Simulation of hyperelastic materials in real-time using Deep Learning
The finite element method (FEM) is among the most commonly used numerical
methods for solving engineering problems. Due to its computational cost,
various ideas have been introduced to reduce computation times, such as domain
decomposition, parallel computing, adaptive meshing, and model order reduction.
In this paper we present U-Mesh: a data-driven method based on a U-Net
architecture that approximates the non-linear relation between a contact force
and the displacement field computed by a FEM algorithm. We show that deep
learning, one of the latest machine learning methods based on artificial neural
networks, can enhance computational mechanics through its ability to encode
highly non-linear models in a compact form. Our method is applied to two
benchmark examples: a cantilever beam and an L-shape subject to moving punctual
loads. A comparison between our method and proper orthogonal decomposition
(POD) is done through the paper. The results show that U-Mesh can perform very
fast simulations on various geometries, mesh resolutions and number of input
forces with very small errors
Diamond-based models for scientific visualization
Hierarchical spatial decompositions are a basic modeling tool in a variety of application domains including scientific visualization, finite element analysis and shape modeling and analysis. A popular class of such approaches is based on the regular simplex bisection operator, which bisects simplices (e.g. line segments, triangles, tetrahedra) along the midpoint of a predetermined edge. Regular simplex bisection produces adaptive simplicial meshes of high geometric quality, while simplifying the extraction of crack-free, or conforming, approximations to the original dataset. Efficient multiresolution representations for such models have been achieved in 2D and 3D by clustering sets of simplices sharing the same bisection edge into structures called diamonds. In this thesis, we introduce several diamond-based approaches for scientific visualization. We first formalize the notion of diamonds in arbitrary dimensions in terms of two related simplicial decompositions of hypercubes. This enables us to enumerate the vertices, simplices, parents and children of a diamond. In particular, we identify the number of simplices involved in conforming updates to be factorial in the dimension and group these into a linear number of subclusters of simplices that are generated simultaneously. The latter form the basis for a compact pointerless representation for conforming meshes generated by regular simplex bisection and for efficiently navigating the topological connectivity of these meshes. Secondly, we introduce the supercube as a high-level primitive on such nested meshes based on the atomic units within the underlying triangulation grid. We propose the use of supercubes to associate information with coherent subsets of the full hierarchy and demonstrate the effectiveness of such a representation for modeling multiresolution terrain and volumetric datasets. Next, we introduce Isodiamond Hierarchies, a general framework for spatial access structures on a hierarchy of diamonds that exploits the implicit hierarchical and geometric relationships of the diamond model. We use an isodiamond hierarchy to encode irregular updates to a multiresolution isosurface or interval volume in terms of regular updates to diamonds. Finally, we consider nested hypercubic meshes, such as quadtrees, octrees and their higher dimensional analogues, through the lens of diamond hierarchies. This allows us to determine the relationships involved in generating balanced hypercubic meshes and to propose a compact pointerless representation of such meshes. We also provide a local diamond-based triangulation algorithm to generate high-quality conforming simplicial meshes
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Hierarchical Large-scale Volume Representation with 3rd-root-of-2 Subdivision and Trivariate B-spline Wavelets
Multiresolution methods provide a means for representing data at multiple levels of detail. They are typically based on a hierarchical data organization scheme and update rules needed for data value computation. We use a data organization that is based on what we call subdivision. The main advantage of subdivision, compared to quadtree (n=2) or octree (n=3) organizations, is that the number of vertices is only doubled in each subdivision step instead of multiplied by a factor of four or eight, respectively. To update data values we use n-variate B-spline wavelets, which yield better approximations for each level of detail. We develop a lifting scheme for n=2 and n=3 based on the -subdivision scheme. We obtain narrow masks that provide a basis for out-of-core techniques as well as view-dependent visualization and adaptive, localized refinement
What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives
Intelligent Mesh Generation (IMG) represents a novel and promising field of
research, utilizing machine learning techniques to generate meshes. Despite its
relative infancy, IMG has significantly broadened the adaptability and
practicality of mesh generation techniques, delivering numerous breakthroughs
and unveiling potential future pathways. However, a noticeable void exists in
the contemporary literature concerning comprehensive surveys of IMG methods.
This paper endeavors to fill this gap by providing a systematic and thorough
survey of the current IMG landscape. With a focus on 113 preliminary IMG
methods, we undertake a meticulous analysis from various angles, encompassing
core algorithm techniques and their application scope, agent learning
objectives, data types, targeted challenges, as well as advantages and
limitations. We have curated and categorized the literature, proposing three
unique taxonomies based on key techniques, output mesh unit elements, and
relevant input data types. This paper also underscores several promising future
research directions and challenges in IMG. To augment reader accessibility, a
dedicated IMG project page is available at
\url{https://github.com/xzb030/IMG_Survey}
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