### 3.9 贝叶斯机器学习与统计物理的联系 (Bayesian Machine Learning and Connections to Statistical Physics) - 讨论了贝叶斯方法在机器学习中的应用,以及它们如何与统计物理的原理相结合。 ### 3.10 动态过程中的学习统计物理 (Statistical Physics of Learning in Dynamic Procedures) - 研究了在动态过程中,如何应...
"When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning." arXiv preprint arXiv:2203.16797 (2022). ^Wang, Rui, and Rose Yu. "Physics-guided deep learning for dynamical systems: A survey." arXiv preprint arXiv:2107.01272 (2021). ^abCano, José-Ramón, et al. ...
AI⧸ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning] 43:27 AI⧸ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning] 36:11 AI⧸ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learni 36:31 AI⧸ML+Physics Part ...
物理信息机器学习(Physics-informed machine learning,PIML),指的是将物理学的先验知识(历史上自然现象和人类行为的高度抽象),与数据驱动的机器学习模型相结合,这已经成为缓解训练数据短缺、提高模型泛化能力和确保结果的物理合理性的有效途径。在本文中,我们...
"Physics-Informed Machine Learning"指的是将物理方程与机器学习相结合的方法,以提高模型的准确性和可解释性。"Artificial Neural Nets that incorporate Rigorous Coupled Wave Analysis into Loss Function"则是指利用人工神经网络,并将严格耦合波分析(Rigorous Coupled Wave Analysis)融入损失函数中。这种方法可以在光学...
近日，发表在 Nature Review Physics杂志上的一篇综述论文「Physics-informed machine learning」提出了「教机器学习物理知识以解决物理问题」的观点。该论文回顾了将物理知识嵌入机器学习的流行趋势，介绍了当前的能力和局限性，并讨论了这类机器学习在发现和解决物理中各种正向、逆向问题中的应用。这篇论文虽然只阐述了...
本研究介绍了PINNacle，这是一个旨在填补这一空白的基准测试工具。PINNacle提供了一个多样化的数据集，包括...
Machine Learning is already being frequently used in computer vision, recommendation systems, medical diagnosis, or financial forecasting. Recently, physics and engineering have also taken advantage of machine learning by tuning these methods for their purpose. This chapter starts with a general review ...
by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), sup...
Such physics- informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel- based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for ...