An Efficient B-Spline Lagrangian/Eulerian Method for Compressible Flow, Shock Waves, and Fracturing Solids

Cao, Yadi; Chen, Yunuo; Li, Minchen; Yang, Yin; Zhang, Xinxin; Aanjaneya, Mridul; Jiang, Chenfanfu

doi:10.1145/3519595

Cited by 6 publications

(3 citation statements)

References 59 publications

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“…Our fracturing approach also simulates the subset of possible fractures that can be generated using subtraction with a randomized geometric primitive, i.e., it does not fully capture the variety and complexity of real fractures. Prior work in physics‐based fracturing is focused on providing visually appealing fractures [BHTF07; CCL∗22; WFL∗19; WDG∗19], is overly simplistic [ESW20; LWL∗21; GBS∗15], or is focused on micro‐scale analysis of fracture from a material science perspective [ANZ06; ZQ18; SMZG18; FLF∗19]. To capture the complexity of real fractures it is necessary to obtain a dataset of real fractured objects.…”

Section: Limitations and Future Workmentioning

confidence: 99%

MendNet: Restoration of Fractured Shapes Using Learned Occupancy Functions

Lamb

Banerjee

2022

Computer Graphics Forum

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We provide a novel approach to perform fully automated generation of restorations for fractured shapes using learned implicit shape representations in the form of occupancy functions. Our approach lays the groundwork to perform automated object repair via additive manufacturing. Existing approaches for restoration of fractured shapes either require prior knowledge of object structure such as symmetries between the restoration and the fractured object, or predict restorations as voxel outputs that are impractical for repair at current resolutions. By leveraging learned occupancy functions for restoration prediction, our approach overcomes the curse of dimensionality with voxel approaches, while providing plausible restorations. Given a fractured shape, we fit a function to occupancy samples from the shape to infer a latent code. We apply a learned transformation to the fractured shape code to predict a corresponding code for restoration generation. To ensure physical validity and well‐constrained shape estimation, we contribute a loss that models feasible occupancy values for fractured shapes, restorations, and complete shapes obtained by joining fractured and restoration shapes. Our work overcomes deficiencies of shape completion approaches adapted for repair, and enables consumer‐driven object repair and cultural heritage object restoration. We share our code and a synthetic dataset of fractured meshes from 8 ShapeNet classes at: https://github.com/Terascale-All-sensing-Research-Studio/MendNet.

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Section: Limitations and Future Workmentioning

confidence: 99%

MendNet: Restoration of Fractured Shapes Using Learned Occupancy Functions

Lamb

Banerjee

2022

Computer Graphics Forum

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“…In light of these developments, MPM represents an effective method for the simulation of a wide range of materials and phenomena. Even though in the literature there are recent GPUs MPM implementations for computer graphics applications [27,28], such implementations have not yet been applied to compressible fluid dynamics; in fact, when addressing gas dynamics and solids mechanics, MPM is used only for the solids, while the compressible fluid dynamic problem is solved through a second order WENO scheme; moreover, such implementation is run on CPU [29]. There exist also some studies on MPM for compressible flows, as [30,31], which focus on modelling and expand the method application field, and as [32], which considers the numerical aspects of the topic.…”

Section: Introductionmentioning

confidence: 99%

GPUs Based Material Point Method for Compressible Flows

Baioni,

Benacchio,

Capone

et al. 2023

VIII International Conference on Particle-Based Methods

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Particle-In-Cell (PIC) methods such as the Material Point Method (MPM) can be cast in formulations suitable to the requirements of data locality and fine-grained parallelism of modern hardware accelerators such as Graphics Processing Units (GPUs). While continuum mechanics simulations have already shown the capabilities of MPM on a wide range of phenomena, the use of the method in compressible gas dynamics is less frequent. This contribution aims to show the potential of a GPU-based MPM parallel implementation for compressible fluid dynamics, as well as to assess the reliability of this approach in reproducing supersonic gas flows against solid obstacles. The results in the paper represent a stepping stone towards a highly parallel, Multi-GPU, MPM-base solver for M ach > 1 Fluid-Structure Interaction problems.

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“…Simulating physical systems through numerically solving partial differential equations (PDEs) plays a key role in various science and engineering applications, ranging from particle-based (Jiang et al, 2016) and mesh-based (Li et al, 2020a) solid mechanics to grid-based fluid (Bridson, 2015) and aero (Cao et al, 2022) dynamics. Despite the extensive successes in improving their stability, accuracy, and efficiency, numerical solvers are often computationally expensive for time-sensitive applications, especially iterative design optimization where fast online inferring is desired.…”

Section: Introductionmentioning

confidence: 99%

Bi-Stride Multi-Scale Graph Neural Network for Mesh-Based Physical Simulation

Cao¹,

Chai²,

Li³

et al. 2022

Preprint

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Learning physical systems on unstructured meshes by flat Graph neural networks (GNNs) faces the challenge of modeling the long-range interactions due to the scaling complexity w.r.t. the number of nodes, limiting the generalization under mesh refinement. On regular grids, the convolutional neural networks (CNNs) with a U-net structure can resolve this challenge by efficient stride, pooling, and upsampling operations. Nonetheless, these tools are much less developed for graph neural networks (GNNs), especially when GNNs are employed for learning large-scale mesh-based physics. The challenges arise from the highly irregular meshes and the lack of effective ways to construct the multi-level structure without losing connectivity. Inspired by the bipartite graph determination algorithm, we introduce Bi-Stride Multi-Scale Graph Neural Network (BSMS-GNN) by proposing bi-stride as a simple pooling strategy for building the multi-level GNN. Bi-stride pools nodes by striding every other BFS frontier; it 1) works robustly on any challenging mesh in the wild, 2) avoids using a mesh generator at coarser levels, 3) avoids the spatial proximity for building coarser levels, and 4) uses non-parametrized aggregating/returning instead of MLPs during pooling and unpooling. Experiments show that our framework significantly outperforms the state-of-the-art method's computational efficiency in representative physics-based simulation cases.

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An Efficient B-Spline Lagrangian/Eulerian Method for Compressible Flow, Shock Waves, and Fracturing Solids

Cited by 6 publications

References 59 publications

MendNet: Restoration of Fractured Shapes Using Learned Occupancy Functions

MendNet: Restoration of Fractured Shapes Using Learned Occupancy Functions

GPUs Based Material Point Method for Compressible Flows

Bi-Stride Multi-Scale Graph Neural Network for Mesh-Based Physical Simulation

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