8. Improvement of scoring functions can be achieved by developing new terms, training on larger high-quality datasets or using sophisticated machine learning-based
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...
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...
例如基于最小化的前向-后向随机神经网络模型来求解耦合的前向随机微分方程；符合各种守恒定律的cPINNs，...
This collection will gather the latest advances in physics-informed machine learning applications in sciences and engineering for real world applications.
Machine LearningQuantum Mechanicslearning neural networksThe author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. ...
Machine Learning for Physics and the Physics of Learning 系列 Workshop: Machine Learning for Physics and the Physics of Learning Tutorials (Schedule) - IPAMWorkshop I: From Passive to Active: Gener…
第3章《统计物理与机器学习》(Statistical Physics and Machine Learning) 探讨了统计物理学的概念和方法如何应用于机器学习领域。以下是该章节的主要内容概述: ### 3.1 引言 (Introduction) - 介绍了统计物理学与机器学习之间的联系,并讨论了这种跨学科合作的潜在价值。
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms: physics-informed neural network (PINN) solving different problems solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] ...
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...