Abstract
In recent years, the application of artificial intelligence to natural science research has gained significant attention, particularly the use of deep learning for solving partial differential equations (PDEs). Traditional methods of solving PDEs are often computationally expensive, time-consuming, and complex to implement. In contrast, deep learning-based methods offer simplicity, flexibility, and efficiency. In this area, physics-informed neural networks (PINNs) have been successfully applied to solve PDEs, demonstrating great potential. Solitary waves are an important component of nonlinear scientific research, with theoretical applications in many fields. In this paper, we improve the original PINNs by incorporating gradient information constraints, which provide stronger physical constraints. Moreover, adaptive learning methods are used to update the weight coefficients of the loss function and dynamically adjust the proportion of each constraint term in the loss function to expedite training and enhance effectiveness. In the experimental component, we apply the improved PINN to the numerical simulation of solitary waves for partial differential equations. In particular, we focus on the Caudrey-Dodd-Gibbon-Sawada-Kotera (CDGSK) equation’s single soliton and multiple soliton solutions and evaluate the accuracy of the solitary wave simulation results using error analysis. The experimental findings demonstrate that the improved PINNs outperform traditional PINNs, particularly in the CDGSK equation, with shorter training times and more accurate prediction results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Guo, Y., Cao, X., Liu, B., Gao, M.: Cloud detection for satellite imagery using attention-based U-Net convolutional neural network. Symmetry 12, 1056 (2020)
Guo, Y., Cao, X., Liu, B., Peng, K.: El Nino index prediction using deep learning with ensemble empirical mode decomposition. Symmetry 12, 893 (2020)
Bourilkov, D.: Machine and deep learning applications in particle physics. Int. J. Mod. Phys. A 34, 1930019 (2019)
Goh, G.B., Hodas, N.O., Vishnu, A.: Deep learning for computational chemistry. J. Comput. Chem. 38, 1291–1307 (2017)
Bryant, P., Pozzati, G., Elofsson, A.: Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun. 13, 1–11 (2022)
Guo, Y., Cao, X., Liu, B., Gao, M.: Solving partial differential equations using deep learning and physical constraints. Appl. Sci. 10, 5917 (2020)
Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019)
Bai, J., Rabczuk, T., Gupta, A., Alzubaidi, L., Gu, Y.: A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics. Comput. Mech. 71, 543–562 (2023)
Cai, S., Mao, Z., Wang, Z., Yin, M., Karniadakis, G.E.: Physics-informed neural networks (PINNs) for fluid mechanics: a review. Acta. Mech. Sin. 37, 1727–1738 (2021)
Biswas, A., Milovic, D., Ranasinghe, A.: Solitary waves of Boussinesq equation in a power law media. Commun. Nonlinear Sci. Numer. Simul. 14, 3738–3742 (2009)
Kaya, D.: A numerical simulation of solitary-wave solutions of the generalized regularized long-wave equation. Appl. Math. Comput. 149, 833–841 (2004)
Yu, J., Lu, L., Meng, X., Karniadakis, G.E.: Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Comput. Methods Appl. Mech. Eng. 393, 114823 (2022)
Xiang, Z., Peng, W., Liu, X., Yao, W.: Self-adaptive loss balanced physics-informed neural networks. Neurocomputing 496, 11–34 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Guo, Y., Cao, X., Peng, K., Tian, W., Zhou, M. (2023). Surrogate Modeling for Soliton Wave of Nonlinear Partial Differential Equations via the Improved Physics-Informed Deep Learning. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_19
Download citation
DOI: https://doi.org/10.1007/978-981-99-4742-3_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4741-6
Online ISBN: 978-981-99-4742-3
eBook Packages: Computer ScienceComputer Science (R0)