A discrete element solution method embedded within a Neural Network
This paper introduces a novel methodology, the Neural Network framework for the Discrete Element Method (NN4DEM), as part of a broader initiative to harness specialised AI hardware and software environments, marking a transition from traditional computational physics programming approaches. NN4DEM e...
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| Published in: | Powder technology Vol. 448; p. 120258 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.12.2024
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| ISSN: | 0032-5910 |
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| Abstract | This paper introduces a novel methodology, the Neural Network framework for the Discrete Element Method (NN4DEM), as part of a broader initiative to harness specialised AI hardware and software environments, marking a transition from traditional computational physics programming approaches. NN4DEM enables GPU-parallelised computations by mapping particle data (coordinates and velocities) onto uniform grids as solution fields and computing contact forces by applying mathematical operations that can be found in convolutional neural networks (CNN). Essentially, this framework transforms a DEM problem into a series of layered “images” composed of pixels, using stencil operations to compute the DEM physics, which is inherently local. The method revolves around custom kernels, with operations prescribed by the laws of physics for contact detection and interaction. Therefore, unlike conventional AI methods, it eliminates the need for training data to determine network weights. NN4DEM utilises libraries such as PyTorch for relatively easier programmability and platform interoperability. This paper presents the theoretical foundations, implementation and validation of NN4DEM through hopper test benchmarks. An analysis of the results from random packing cases highlights the ability of NN4DEM to scale to 3D models with millions of particles. The paper concludes with potential research directions, including further integration with other physics-based models and applications across various multidisciplinary fields.
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•A novel integration: neural networks with DEM principles.•Physical laws dictate network weights.•Neural Network solver benefits without training data.•GPU parallelisation with PyTorch.•Random packing demonstrates computational capacity. |
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| AbstractList | This paper introduces a novel methodology, the Neural Network framework for the Discrete Element Method (NN4DEM), as part of a broader initiative to harness specialised AI hardware and software environments, marking a transition from traditional computational physics programming approaches. NN4DEM enables GPU-parallelised computations by mapping particle data (coordinates and velocities) onto uniform grids as solution fields and computing contact forces by applying mathematical operations that can be found in convolutional neural networks (CNN). Essentially, this framework transforms a DEM problem into a series of layered “images” composed of pixels, using stencil operations to compute the DEM physics, which is inherently local. The method revolves around custom kernels, with operations prescribed by the laws of physics for contact detection and interaction. Therefore, unlike conventional AI methods, it eliminates the need for training data to determine network weights. NN4DEM utilises libraries such as PyTorch for relatively easier programmability and platform interoperability. This paper presents the theoretical foundations, implementation and validation of NN4DEM through hopper test benchmarks. An analysis of the results from random packing cases highlights the ability of NN4DEM to scale to 3D models with millions of particles. The paper concludes with potential research directions, including further integration with other physics-based models and applications across various multidisciplinary fields. This paper introduces a novel methodology, the Neural Network framework for the Discrete Element Method (NN4DEM), as part of a broader initiative to harness specialised AI hardware and software environments, marking a transition from traditional computational physics programming approaches. NN4DEM enables GPU-parallelised computations by mapping particle data (coordinates and velocities) onto uniform grids as solution fields and computing contact forces by applying mathematical operations that can be found in convolutional neural networks (CNN). Essentially, this framework transforms a DEM problem into a series of layered “images” composed of pixels, using stencil operations to compute the DEM physics, which is inherently local. The method revolves around custom kernels, with operations prescribed by the laws of physics for contact detection and interaction. Therefore, unlike conventional AI methods, it eliminates the need for training data to determine network weights. NN4DEM utilises libraries such as PyTorch for relatively easier programmability and platform interoperability. This paper presents the theoretical foundations, implementation and validation of NN4DEM through hopper test benchmarks. An analysis of the results from random packing cases highlights the ability of NN4DEM to scale to 3D models with millions of particles. The paper concludes with potential research directions, including further integration with other physics-based models and applications across various multidisciplinary fields. [Display omitted] •A novel integration: neural networks with DEM principles.•Physical laws dictate network weights.•Neural Network solver benefits without training data.•GPU parallelisation with PyTorch.•Random packing demonstrates computational capacity. |
| ArticleNumber | 120258 |
| Author | Pain, Christopher C. Yang, Tongan Naderi, Sadjad Chen, Boyang Latham, John-Paul Xiang, Jiansheng Wang, Yanghua Heaney, Claire E. |
| Author_xml | – sequence: 1 givenname: Sadjad orcidid: 0000-0003-0721-3749 surname: Naderi fullname: Naderi, Sadjad organization: Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK – sequence: 2 givenname: Boyang orcidid: 0000-0002-9091-0352 surname: Chen fullname: Chen, Boyang email: boyang.chen16@imperial.ac.uk organization: Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK – sequence: 3 givenname: Tongan surname: Yang fullname: Yang, Tongan organization: Resource Geophysics Academy, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK – sequence: 4 givenname: Jiansheng surname: Xiang fullname: Xiang, Jiansheng organization: Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK – sequence: 5 givenname: Claire E. surname: Heaney fullname: Heaney, Claire E. organization: Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK – sequence: 6 givenname: John-Paul surname: Latham fullname: Latham, John-Paul organization: Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK – sequence: 7 givenname: Yanghua surname: Wang fullname: Wang, Yanghua organization: Resource Geophysics Academy, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK – sequence: 8 givenname: Christopher C. surname: Pain fullname: Pain, Christopher C. email: c.pain@imperial.ac.uk organization: Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK |
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