物理神经网络（PINN）论文推荐：经典研究与应用

当涉及到物理神经网络（PINN）的相关论文时，以下是几篇经典且具有代表性的研究，供您参考：

Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics, 378, 686-707.
Sirignano, J., & Spiliopoulos, K. (2018). DGM: A deep learning algorithm for solving partial differential equations. Journal of Computational Physics, 375, 1339-1364.
Lu, L., & Meng, X. (2020). Physics constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. Journal of Computational Physics, 404, 109126.
Wang, Q., Shen, J., Chen, L., & Wei, G. W. (2020). Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics. Physical Review Letters, 125(7), 070601.
Zhang, L., Zhang, J., & Zhang, X. (2020). Adversarial Physics-Informed Neural Networks for High-Dimensional Partial Differential Equations. Journal of Computational Physics, 414, 109466.

这些论文代表了物理神经网络在不同领域和问题上的应用，如流体力学、固体力学、量子力学等。阅读这些论文将使您对PINN的原理、应用和方法有更深入的了解，并为您进一步的研究提供指导和启示。