Author:
Song Xiaotian,Deng Shuchao,Fan Jiahao,Sun Yanan
Publisher
Springer Nature Singapore
Reference42 articles.
1. Tagliabue, A., Dede, L., Quarteroni, A.: Isogeometric analysis and error estimates for high order partial differential equations in fluid dynamics. Comput. Fluids 102, 277–303 (2014)
2. Wessels, H., Weißenfels, C., Wriggers, P.: The neural particle method–an updated lagrangian physics informed neural network for computational fluid dynamics. Comput. Methods Appl. Mech. Eng. 368, 113127 (2020)
3. Naz, R., Mahomed, F.M., Mason, D.P.: Comparison of different approaches to conservation laws for some partial differential equations in fluid mechanics. Appl. Math. Comput. 205(1), 212–230 (2008)
4. Haghighat, E., Raissi, M., Moure, A., Gomez, H., Juanes, R.: A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics. Comput. Methods Appl. Mech. Eng. 379, 113741 (2021)
5. Ikawa, M.: Hyperbolic partial differential equations and wave phenomena, vol. 2. American Mathematical Soc. (2000)