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Koyamada, Koji*; Yu, L.*; Kawamura, Takuma; Konishi, Katsumi*
International Journal of Modeling, Simulation, and Scientific Computing, 12(2), p.2140001_1 - 2140001_19, 2021/04
With the improvement of sensors technologies in various fields such as fluid dynamics, meteorology, and space observation, it is an important issue to derive explanatory models using partial differential equations (PDEs) for the big data obtained from them. In this paper, we propose a technique for estimating linear PDEs with higher-order derivatives for spatiotemporally discrete point cloud data. The technique calculates the time and space derivatives from a neural network (NN) trained on the point cloud data, and estimates the derivative term of the PDE using regression analysis techniques. In the experiment, we computed the error of the estimated PDEs for various meta-parameters for the PDEs with exact solutions. As a result, we found that increasing the hierarchy of NNs to match the order of the derivative terms in the exact solution PDEs and adopting L1 regularization with LASSO as the method of regression analysis increased the accuracy of the model.
Kawamura, Takuma; Idomura, Yasuhiro; Miyamura, Hiroko; Takemiya, Hiroshi; Sakamoto, Naohisa*; Koyamada, Koji*
Visualization and Data Analysis 2015 (Proceedings of SPIE Vol.9397) (Internet), p.93970S_1 - 93970S_8, 2015/02
Times Cited Count：4 Percentile：87.56(Computer Science, Theory & Methods)In this paper, we propose a novel remote visualization system based on particle-based volume rendering (PBVR), which enables interactive analyses of extreme scale volume data located on remote computing systems. The remote PBVR system consists of Server, which generates particles for rendering, and Client, which processes volume rendering, and the particle data size becomes significantly smaller than the original volume data. Depending on network bandwidth, the level of detail of images is flexibly controlled to attain high frame rates. Server is highly parallelized on various parallel platforms with hybrid programing model. The mapping process is accelerated by two orders of magnitudes compared with a single CPU. The structured and unstructured volume data with 100 millions of cells is processed within a few seconds. Compared with commodity Client/Server visualization tools, the total processing cost is dramatically reduced by using proposed system.
Suzuki, Yoshio*; Takeshima, Yuriko; Ono, Nobuaki*; Koyamada, Koji*
Nihon Bacharu Riaritei Gakkai Rombunshi, 10(2), p.231 - 240, 2005/06
A volume rendering is widely used for intuitively understanding 3-dimensionaly distribution of physical quantities. When the quantities have a nest-like distribution, however, the inside distribution cannot be observed. As one of the solution, an immersive virtual reality (VR) system is useful, since the researcher can immersively observe the distribution by using such a system. However, a plane slice sampling method conventionally used in the volume rendering has a problem that the quality of visualized images deteriorates especially in the immersive VR system. To resolve the problem, a spherical surface sampling method is applied to the volume rendering in the immersive VR system. The quality of image and the display speed are compared between these two methods.
Suzuki, Yoshio; Sai, Kazunori*; Ono, Nobuaki*; Koyamada, Koji*
Kashika Joho Gakkai-Shi, 24(Suppl.1), p.443 - 446, 2004/07
no abstracts in English
Otagiri, Sadanori*; Hirota, Katsuhiko*; Suzuki, Yoshio; Watashiba, Yasuhiro*; Koyamada, Koji*
Kashika Joho Gakkai-Shi, 24(Suppl.1), p.455 - 456, 2004/07
no abstracts in English
