A Learning Acceleration of Image-based Surrogate Models Using Pixel Shuffling
Research on surrogate models, which are expected to significantly reduce computational time by replacing large-scale numerical simulations that require a large amount of computational time with approximate models such as machine learning models, has been attracting attention. However, if the numeric...
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| Published in | Transaction of the Japan Society for Simulation Technology Vol. 16; no. 2; pp. 48 - 59 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | Japanese |
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Japan Society for Simulation Technology
2024
日本シミュレーション学会 |
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| Online Access | Get full text |
| ISSN | 1883-5031 1883-5058 |
| DOI | 10.11308/tjsst.16.48 |
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| Abstract | Research on surrogate models, which are expected to significantly reduce computational time by replacing large-scale numerical simulations that require a large amount of computational time with approximate models such as machine learning models, has been attracting attention. However, if the numerical data generated by the simulations is large, the data generated by the surrogate model will also be large, which may cause time consuming in visualization processing during analysis. If a surrogate model, which we call image-based surrogate model, that also takes visualization processing into consideration can be constructed for large-scale simulation results, it will be possible to directly generate visualization results without simulation data, which is expected to greatly improve efficiency in analysis through visualization. In this study, an image-based surrogate model is constructed by learning multiple visualization images of numerical data and the simulation parameters at that time. The learning model to be developed is constructed based on the adversarial generative network model, and pixel shuffling is applied to the generators that are part of the model to make feature extraction more efficient, thereby speeding up the convergence of learning loss. In our experiments, we applied this method to actual numerical simulations and succeeded in speeding up the process by a factor of approximately 2.7 while maintaining the same level of prediction accuracy as existing learning models. |
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| AbstractList | Research on surrogate models, which are expected to significantly reduce computational time by replacing large-scale numerical simulations that require a large amount of computational time with approximate models such as machine learning models, has been attracting attention. However, if the numerical data generated by the simulations is large, the data generated by the surrogate model will also be large, which may cause time consuming in visualization processing during analysis. If a surrogate model, which we call image-based surrogate model, that also takes visualization processing into consideration can be constructed for large-scale simulation results, it will be possible to directly generate visualization results without simulation data, which is expected to greatly improve efficiency in analysis through visualization. In this study, an image-based surrogate model is constructed by learning multiple visualization images of numerical data and the simulation parameters at that time. The learning model to be developed is constructed based on the adversarial generative network model, and pixel shuffling is applied to the generators that are part of the model to make feature extraction more efficient, thereby speeding up the convergence of learning loss. In our experiments, we applied this method to actual numerical simulations and succeeded in speeding up the process by a factor of approximately 2.7 while maintaining the same level of prediction accuracy as existing learning models.
多大な計算時間を必要とする大規模数値シミュレーションを機械学習モデルなどの近似モデルで置き換えることにより，大幅な計算時間短縮が期待されるサロゲートモデルに関する研究が注目されている．しかし，シミュレーションによって出力される数値データが大規模であった場合は，サロゲートモデルによって出力されるデータも大規模となり，解析時の可視化処理が困難な状況が発生する可能性がある．大規模なシミュレーション結果を対象にした場合，可視化処理も考慮したサロゲートモデル（画像ベースサロゲートモデル）が構築できれば，数値データを出力することなく可視化結果画像を直接出力できるようになり，可視化を介した解析において大幅な効率化が期待できる．本研究では，数値データを対象とする複数の可視化画像と，そのときのシミュレーションパラメータを一緒に学習することで，画像ベースサロゲートモデルを構築する．開発する学習モデルは，敵対的生成ネットワークモデルをもとにして構成され，その一部である生成器に対して，特徴抽出を効率化するためにピクセルシャッフルを適用することで，学習損失の収束を高速化する．実験では，本手法を実際の数値シミュレーションに適用し，既存の学習モデルと同程度の予測精度を保ちつつ約2.7倍の高速化に成功した． Research on surrogate models, which are expected to significantly reduce computational time by replacing large-scale numerical simulations that require a large amount of computational time with approximate models such as machine learning models, has been attracting attention. However, if the numerical data generated by the simulations is large, the data generated by the surrogate model will also be large, which may cause time consuming in visualization processing during analysis. If a surrogate model, which we call image-based surrogate model, that also takes visualization processing into consideration can be constructed for large-scale simulation results, it will be possible to directly generate visualization results without simulation data, which is expected to greatly improve efficiency in analysis through visualization. In this study, an image-based surrogate model is constructed by learning multiple visualization images of numerical data and the simulation parameters at that time. The learning model to be developed is constructed based on the adversarial generative network model, and pixel shuffling is applied to the generators that are part of the model to make feature extraction more efficient, thereby speeding up the convergence of learning loss. In our experiments, we applied this method to actual numerical simulations and succeeded in speeding up the process by a factor of approximately 2.7 while maintaining the same level of prediction accuracy as existing learning models. |
| Author | Sakamoto, Naohisa Miyake, Tomoya Tanaka, Yuki |
| Author_FL | 坂本 尚久 田中 祐希 三宅 智也 |
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| References_xml | – reference: 18) W. He, J. Wang, H. Guo, K.-C. Wang, H.-W. Shen, M. Raj, Y. S. G. Nashed and T. Peterka: Insitunet: Deep image synthesis for parameter space exploration of ensemble simulations, IEEE Transactions on Visualization and Computer Graphics, 26-1, 23/33 (2019) – reference: 21) W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert and Z. Wang: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1874/1883 (2016) – reference: 10) S. Cheng, I. C. Prentice, Y. Huang, Y. Jin, Y.-K. Guo and R. Arcucci: Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting, Journal of Computational Physics, 464, 111302 (2022) – reference: 8) N. V. Queipo, R. T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan and P. Kevin Tucker: Surrogate-based analysis and optimization, Progress in Aerospace Sciences, 41-1, 1/28 (2005) – reference: 25) R. Zhang, P. Isola, A. A. Efros, E. Shechtman and O. Wang: The unreasonable effectiveness of deep features as a perceptual metric, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 586/595 (2018) – reference: 5) K. Imadera and Y. Kishimoto: Itb formation in gyrokinetic flux-driven itg/tem turbulence, Plasma Physics and Controlled Fusion, 65-2, 024003 (2022) – reference: 12) N. Shi, J. Xu, S. W. Wurster, H. Guo, J. Woodring, L. P. Van Roekel and H.-W. Shen: Gnn-surrogate: A hierarchical and adaptive graph neural network for parameter space exploration of unstructured-mesh ocean simulations, IEEE Transactions on Visualization and Computer Graphics, 28-6, 2301/2313 (2022) – reference: 17) J. Han and C. Wang: Coordnet: Data generation and visualization generation for time-varying volumes via a coordinate-based neural network, IEEE Transactions on Visualization and Computer Graphics (2022) – reference: 7) 三島英彦, 高橋健一郎, 中村悠一, 寺尾岳見, 島田茂樹：製品開発・モノづくりを支えるcae技術，JETI= ジェティ: Japan energy & technology intelligence: エネルギー・化学・プラントの総合技術誌，70-12, 97/102 (2022) – reference: 9) 出口翔大, 浅井光輝, 植木裕人, 竹内友紀, 川崎浩司：数値解析のサロゲートモデリングによる確率論的災害リスク評価手法の開発，土木学会論文集A2 (応用力学)，76-2, I_565/I_576 (2020) – reference: 14) T. Wang, M. Shao, R. Guo, F. Tao, G. Zhang, H. Snoussi and X. Tang: Surrogate model via artificial intelligence method for accelerating screening materials and performance prediction, Advanced Functional Materials, 31-8, 2006245 (2021) – reference: 1) 藤堂眞治ら：計算科学ロードマップ2023, 12 2023. Available online: https://cs-forum.github.io/roadmap-2023/ – reference: 11) M. T. Contreras, J. Gironás and C. Escauriaza: Forecasting flood hazards in real time: a surrogate model for hydrometeorological events in an andean watershed, Natural Hazards and Earth System Sciences, 20-12, 3261/3277 (2020) – reference: 19) K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770/778 (2016) – reference: 22) T. Miyato and M. Koyama: cgans with projection discriminator, arXiv preprint arXiv:1802.05637 (2018) – reference: 2) T. Ichimura, K. Fujita, K. Koyama, R. Kusakabe, Y. Kikuchi, T. Hori, M. Hori, L. Maddegedara, N. Ohi, T. Nishiki et al.: 152k-computer-node parallel scalable implicit solver for dynamic nonlinear earthquake simulation, International Conference on High Performance Computing in Asia-Pacific Region, 18/29 (2022) – reference: 13) N. Shi, J. Xu, H. Li, H. Guo, J. Woodring and H.-W. Shen: Vdl-surrogate: A view-dependent latent-based model for parameter space exploration of ensemble simulations, IEEE Transactions on Visualization and Computer Graphics, 29-1, 820/830 (2022) – reference: 23) K. Simonyan and A. Zisserman: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014) – reference: 16) F. Hong, C. Liu and X. Yuan: Dnn-volvis: Interactive volume visualization supported by deep neural network, 2019 IEEE Pacific Visualization Symposium (PacificVis), 282/291, IEEE (2019) – reference: 24) A. Kageyama and N. Sakamoto: 4d street view: a video-based visualization method, PeerJ Computer Science, 6, e305 (2020) – reference: 6) 気象庁：2030年に向けた数値予報技術開発重点計画．Technical report，気象庁，10 2018. Available online: https://www.jma.go.jp/jma/index.html – reference: 3) A. Brunelli, F. De Silva, A. Piro, F. Parisi, S. Sica, F. Silvestri and S. Cattari: Numerical simulation of the seismic response and soil–structure interaction for a monitored masonry school building damaged by the 2016 central italy earthquake, Bulletin of Earthquake Engineering, 19-2, 1181/1211 (2021) – reference: 4) J. Klimo, M. Veselsky, G. A. Souliotis and A. Bonasera: Simulation of fusion and quasi-fission in nuclear reactions leading to production of superheavy elements using the constrained molecular dynamics model, Nuclear Physics A, 992, 121640 (2019) – reference: 15) J. Weiss and N. Navab: Deep direct volume rendering: Learning visual feature mappings from exemplary images, arXiv preprint arXiv:2106.05429 (2021) – reference: 20) T. Miyato, T. Kataoka, M. Koyama and Y. Yoshida: Spectral normalization for generative adversarial networks, arXiv preprint arXiv:1802.05957 (2018) |
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| SubjectTerms | Deep Learning Image Synthesis Numerical Simulation Surrogate Model Visualization サロゲートモデル 可視化 数値シミュレーション 深層学習 画像合成 |
| Title | A Learning Acceleration of Image-based Surrogate Models Using Pixel Shuffling |
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