0 总栏

期刊名词	分区
Multimedia Tools and Applications	4	2.33
Neural computing and application	2	5.6
Computer methods in applied mechanics and engineering	2	5.763
SIAM journal on scientific computing	2	1.97
Communication in Computational Physics	3	2.6
PLoS One（公共科学图书馆）	3
Frontiers in Physics	3	2.638
Frontiers of Information Technology & Electronic Engineering	3	2.161
Nonlinear Dynamics	1	5.022
Applied Thermal Engineering	2	5.295

1 Multimedia Tools and Applications

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Deep learning-based method coupled with small sample learning for solving partial differential equations

特点

看评审周期略长，容易中，创新点要求不是很高

2 NEURAL COMPUTING & APPLICATIONS

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[1] A. R. Brink, D. A. Najera-Flores, and C. Martinez, “The neural network collocation method for solving partial differential equations,” Neural Comput. Appl., vol. 5, 2020, doi: 10.1007/s00521-020-05340-5.
[2] V. I. Avrutskiy, “Neural networks catching up with finite differences in solving partial differential equations in higher dimensions,” Neural Comput. Appl., vol. 32, no. 17, pp. 13425–13440, 2020, doi: 10.1007/s00521-020-04743-8.

3 COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

![image.png](https://img-blog.csdnimg.cn/img_convert/23de1ef4cc6cca06129813cdbdde5923.png#crop=0&crop=0&crop=1&crop=1&height=200&id=FYZCw&margin=[object Object]&name=image.png&originHeight=400&originWidth=1099&originalType=binary&ratio=1&rotation=0&showTitle=false&size=43012&status=done&style=none&title=&width=549.5)
[1] H. Wessels, C. Weißenfels, and P. Wriggers, “The neural particle method – An updated Lagrangian physics informed neural network for computational fluid dynamics,” Comput. Methods Appl. Mech. Eng., vol. 368, p. 113127, 2020, doi: 10.1016/j.cma.2020.113127.
[2] M. Liu, L. Liang, and W. Sun, “A generic physics-informed neural network-based constitutive model for soft biological tissues,” Comput. Methods Appl. Mech. Eng., vol. 372, no. September, p. 113402, 2020, doi: 10.1016/j.cma.2020.113402.

4 SIAM JOURNAL ON SCIENTIFIC COMPUTING

5 Communications in Computational Physics

![image.png](https://img-blog.csdnimg.cn/img_convert/2bcf4d3425e631cbd2cc9bbf003c877f.png#crop=0&crop=0&crop=1&crop=1&height=363&id=DoptP&margin=[object Object]&name=image.png&originHeight=725&originWidth=1187&originalType=binary&ratio=1&rotation=0&showTitle=false&size=53862&status=done&style=none&title=&width=593.5)
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[1] A. D. J. & G. E. Karniadakis, “Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations,” Commun. Comput. Phys., vol. 28, no. 5, pp. 2002–2041, 2020, doi: 10.4208/cicp.oa-2020-0164.
[2] E. Weinan and B. Yu, “The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems,” Commun. Math. Stat., vol. 6, no. 1, pp. 1–14, 2018, doi: 10.1007/s40304-018-0127-z.

6 PLoS One

7 Frontiers in Physics

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[1] F. Sahli Costabal, Y. Yang, P. Perdikaris, D. E. Hurtado, and E. Kuhl, “Physics-Informed Neural Networks for Cardiac Activation Mapping,” Front. Phys., vol. 8, no. February, pp. 1–12, 2020, doi: 10.3389/fphy.2020.00042.