本文所述的物理先验(Physics Priors)是比较广义的理解,并且下文将根据先验信息的强弱程度将其分为:经典AI模型、性质先验嵌入(形状先验、不变/等变/对称/反对称性)、公式形式嵌入、可微物理模型,并分别进行阐释和分析。一些相关的survey:[2][3][4][5][6][7]。 虽然嵌入物理先验可以带来上述好处(数据效率、泛化...
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如...
Machine learning used to represent physics-based and/or engineering modelsBenchmarks Add a Result These leaderboards are used to track progress in Physics-informed machine learning No evaluation results yet. Help compare methods by submitting evaluation metrics. ...
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 ...
github 上面整理的一个 repo：https://github.com/csjiezhao/Physics-Based-Deep-Learning
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...
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 ...
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 ...
Nobel Prize in physics awarded to 2 scientists for discoveries in machine learning From: AP NEWS John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in physics Tuesday for discoveries and inventions that formed ...
This collection will gather the latest advances in physics-informed machine learning applications in sciences and engineering for real world applications.