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Maziar raissi associate professor of applied mathematics, university of california riverside 在 ucr.edu 的电子邮件经过验证 融合物理信息的神经网络（Physics Informed Neural Network, PINN）由布朗大学Raissi M等人提出，相关的研究成果发表于JCP（截止目前引用次数超过5000次）: Extensions to nonlinear problems were proposed in subsequent studies by raissi et al
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[8], [9] in the context of both inference and systems identification Raissi教授是IFAC模型、辨识及信号处理技术委员会委员，也是IEEE高级会员。 他的研究方向包括故障检测与隔离、非线性系统估计与鲁棒控制。 Rassi教授在IEEE Transactions on Automatic Control和Automatica等相关顶级期刊发表多篇论文，包括多篇高被引论文。 Despite the flexibility and mathematical elegance of gaussian processes in encoding prior information, the treatment of nonlinear problems introduces two important limitations.
Raissi, maziar, paris perdikaris, and george e
A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Raissi等人 [146]介绍并说明了PINN方法求解非线性偏微分方程，如Schrödinger、Burgers和Allen-Cahn方程。 他们创建了物理神经网络 (pinn)，既可以处理估计控制数学模型解的正向问题，也可以处理从可观察数据中学习模型参数的逆问题。 Maziar raissi department of mathematics, university of california, riverside google scholar our lab’s research lies at the intersection of scientific computing and artificial intelligence (ai), with a focus on integrating foundational first principles into ai models to address complex challenges in science and engineering. M. Raissi , P. Perdikaris , G.E. Karniadakis 这篇文章是从在一篇文献（Physics-Informed Neural Networks for Power Systems）中反复出现的，参考了里面的很多，所以打算拿出来看看。
