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个人简介

2014年于武汉大学获学士学位，2019年获得武汉大学博士学位，曾在华为技术有限公司、虎牙科技有限公司和武汉大学从事算法研究工作。目前主要关注固体材料的多尺度建模，深度学习在科学计算与数据科学中的应用与理论。

开设课程

高等数学D

研究方向

计算物理与深度学习

教育经历

2014 - 2019 理学博士 武汉大学数学与统计学院 计算数学
2010 - 2014 理学学士 武汉大学数学与统计学院 数学弘毅班

工作经历

2024.06 - 至      今 副研究员 华中师范大学
2022.05 - 2024.05 助理研究员/重点资助博士后 武汉大学
2019.11 - 2022.05 算法研究员/虎牙风控算法组长 广州虎牙科技有限公司
2019.06 - 2019.11 算法工程师 华为技术有限公司

研究成果

Accepted:
1. Sheng, R., Wu, P., Yang, J. Z., & Yuan, C. (2024). Solving the inverse source problem of the fractional Poisson equation by MC-fPINNs. East Asian Journal on Applied Mathematics, Accepted.
2. Jiao, Y., Liu, Y., Yang, J. Z., & Yuan, C. (2024). A Stabilized Physics Informed Neural Networks Method for Wave Equations. Numerical Mathematics: Theory, Methods and Applications, Online.
3. Jiao, Y., Li, D., Lu, X., Yang, J. Z., & Yuan, C. (2024). A Gaussian mixture distribution-based adaptive sampling method for physics-informed neural networks. Engineering Applications of Artificial Intelligence, 135, 108770.
4. Huang, Q. A., Yuan, C., Zhang, G., & Zhang, L. (2024). A computationally optimal relaxed scalar auxiliary variable approach for solving gradient flow systems. Computers & Mathematics with Applications, 156, 64-73.
5. Huang, Q. A., Jiang, W., Yang, J. Z., & Yuan, C. (2023). A structure-preserving, upwind-SAV scheme for the degenerate Cahn–Hilliard equation with applications to simulating surface diffusion. Journal of Scientific Computing, 97(3), 64.
6. Jiao, Y., Yang, J. Z., Yuan, C. & Zhou, J. (2023). A Rate of Convergence of Weak Adversarial Neural Networks for the Second Order Parabolic PDEs. Communications in Computational Physics, 34(3), 813-836.
7. Jiao, Y., Lu, X., Yang, J. Z., Yuan, C. & Zhang, P. (2023). Improved Analysis of PINNs: Alleviate the CoD for Compositional Solutions. Annals of Applied Mathematics, 39(3), 239-263.
8. Li, C., Liu, Y., Wang, F., Yang, J. Z., & Yuan, C. (2023). Absorbing Interface Conditions for the Simulation of Wave Propagation on Non-Uniform Meshes. Numerical Mathematics: Theory, Methods and Applications, 16(2), 433-452.
9. Hu, T., Yang, J. Z., & Yuan, C. (2023). DPK: Deep Neural Network Approximation of the First Piola-Kirchhoff Stress. Advances in Applied Mathematics and Mechanics, 16(1), 75-100.
10. Dai, S., Sun, Z., Wang, F., Yang, J. Z., & Yuan, C. (2022). Hierarchical Absorbing Interface Conditions for Wave Propagation on Non-Uniform Meshes. Numerical Mathematics: Theory, Methods and Applications, 15(1), 251-278.
11. Dai, S., Wang, F., Yang, J. Z., & Yuan, C. (2022). On the Cauchy-Born approximation at finite temperature for alloys. Discrete and Continuous Dynamical Systems-B, 27(6), 3131-3153.
12. Luo, T., Xiang, Y., Yang, J. Z., & Yuan, C. (2021). Cauchy-Born rule and stability of crystalline solids at finite temperature. Communications in Mathematical Sciences, 19(6), 1461-1490.
13. Dai, S., Wang, F., Yang, J. Z., & Yuan, C. (2021). A comparative study of atomistic-based stress evaluation. Discrete and Continuous Dynamical Systems-B, 26(9), 4999-5021.
14. Dai, S., Wang, F., Xiang, Y., Yang, J. Z., & Yuan, C. (2021). Boundary Condition for Dislocation Dynamic Simulation in BCC Crystal. CSIAM Transactions on Applied Mathematics, 2(1), 175-194.
15. Wang, F., Yang, J. Z., & Yuan, C. (2020). Practical absorbing boundary conditions for wave propagation on arbitrary domain. Advances in Applied Mathematics and Mechanics, 12(6), 1384-1415.
16. Liu, P., Yang, J. Z., & Yuan, C. (2020). Extended Synchronous Variational Integrators for Wave Propagations on Non-Uniform Meshes. Communications in Computational Physics, 28(2), 691-722.
17. Yuan, C., Yang, J. Z., & Li, X. (2019). An Effective and Easy-to-Implement Boundary Condition for Molecular Dynamics Simulations. Communications in Computational Physics, 26(1), 192-205.

In preparation:
1. On the Recovery of Source Term for Fractional Evolution PDEs by MC-fPINNs
2. Score-Based Sequential Langevin Sampling for Data Assimilation
3. Convergence Analysis of WAN with Weaker Conditions
4. Sampling via Föllmer Flow
5. A weighted scalar auxiliary variable method for solving gradient flows

详见：https://www.researchgate.net/profile/Cheng-Yuan-5

研究项目

1. 国家自然科学基金委员会，青年科学基金项目，柯西波恩准则在复杂材料多尺度建模中的应用研究，12301558，主持
2. 国家自然科学基金委员会，面上项目，非一致网格求解波动方程的吸收界面条件研究，11671312，参与

附件

袁成

English Version (英文版)

袁 成

English Version (英文版)

袁成