Currently, the prediction of SSC could be mainly divided into two methods: physics-based modeling based on ocean dynamical equations and deep learning approaches. The physics-based modeling method, based on ocean dynamical equations, involved establishing mathematical models that included continuity equatio...
首先,学员将深入理解深度学习的基本原理和常见算法,掌握神经网络、卷积神经网络等模型的应用,能够在疲劳与断裂分析中灵活运用深度学习方法。其次,学员将掌握疲劳与断裂力学的基本理论,理解疲劳裂纹扩展、断裂韧性、疲劳寿命预测等关键内容,并能够...
the limitation of the current wind measurement technology and the need of spatiotemporal wind information in various applications, by developing a deep learning based method that can predict the spatiotemporal wind field in the whole flow domain through combining LIDAR measurement and flow physics. ...
Deep Learning Method Based on Physics-Informed Neural Network for 3D Anisotropic Steady-State Heat Conduction ProblemsSTEADY state conductionDEEP learningHEAT conductionNUMERICAL solutions to partial differential equationsPROBLEM-based learningThis paper uses the physical information neural network (PINN) model ...
This paper uses the physical information neural network (PINN) model to solve a 3D anisotropic steady-state heat conduction problem based on deep learning techniques. The model embeds the problem’s governing equations and boundary conditions into the neural network and treats the neural network’s ...
Deep learning is a specialized form of machine learning, and both are part of the artificial intelligence (AI) field. Machine learning offers a variety of techniques and models you can choose based on your application, the size of data you are processing, and the type of problem you want to...
Physics-Based Deep Learning The following collection of materials targets "Physics-Based Deep Learning" (PBDL), i.e., the field of methods with combinations of physical modeling and deep learning (DL) techniques. Here, DL will typically refer to methods based on artificial neural networks. The...
physics-based learning tasks, leading to increased errors when encountering out-of-distribution (OOD) data. In this work, motivated by the field of functional data analysis (FDA), we propose generalized functional linear models as an interpretable surrogate for a trained deep learning model. We ...
and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data...
As retinal vasculature is positioned in front of the retina itself, we optimised networks to minimise the area of the retina occluded by vessels, according to a cost function based on Murray’s law: $$C\left(B,\,\lambda,\,\rho \right)={\sum}_{b\in B}\,{r}_{b}^{\rho }{l}_...