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We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution. | We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution. | ||
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| + | <pdf>Media:Draft_Sanchez Pinedo_914338648119.pdf</pdf> | ||
Latest revision as of 13:41, 1 July 2024
Abstract
We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution.
Full Paper
Document information
Published on 01/07/24
Accepted on 01/07/24
Submitted on 01/07/24
Volume Data Science, Machine Learning and Artificial Intelligence, 2024
DOI: 10.23967/wccm.2024.119
Licence: CC BY-NC-SA license
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