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…
Big data and associated algorithms, coalesced under the field of machine learning (ML), offer the opportunity to study the physics of the climate system in ways, and with an amount of detail, that were previously infeasible. Additionally, ML can ask causal questions to determine whether one or...
et al. Machine learning at the energy and intensity frontiers of particle physics. Nature 560, 41–48 (2018). Article ADS Google Scholar Feickert, M. & Nachman, B. A living review of machine learning for particle physics. Preprint at arXiv https://arxiv.org/abs/2102.02770 (2021)....
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...
乍一看，训练一个深度学习算法以从几个输入和输出数据开始，到最后可以准确识 别非线性映射的任务，这是...
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...
This collection will gather the latest advances in physics-informed machine learning applications in sciences and engineering for real world applications.
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...
"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational physics 378 (2019): 686-707. ^Lu, Lu, et al. "DeepXDE: A deep learning library for solving differential equations." ...
In the physics-informed machine learning based RB method, both physics and data are used to train a network that maps the time-parameter value to the reduced coefficients. The loss function for the network training is the weighted sum of the mean squared norm of the residual of the POD-G ...