Deep Learning Method Based on Physics-Informed Neural Network for 3D Anisotropic Steady-State Heat Conduction Problemsdoi:10.3390/math11194049STEADY state conductionDEEP learningHEAT conductionNUMERICAL solutions to partial differential equationsPROBLEM-based learning...
The proposed physics-guided deep learning neural network (PGDLNN) for structural damage identification is constructed based on the classical deep learning neural networks, i.e., convolutional neural networks, and modal parameter sensitivity analysis. The proposed approach is developed to achieve a good...
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. ...
Most of the previous works are purely data‐driven or dynamics‐based methods. In order to employ the advantages of dynamic and data‐driven models at the same time, a physics‐informed deep operator learning algorithm is proposed to reconstruct the density field. The proposed method shows ...
A physics-based deep learning (DL) method termed Phynet is proposed for modeling the nonlinear pulse propagation in optical fibers totally independent of the ground truth. The presented Phynet is a combination of a handcrafted neural net... H Sui,H Zhu,B Luo,... - 《Optics Letters》 被...
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 gen...
method can address anisotropy that is governed by a mixture of multiple image degrading factors, many of which are not simply modeled with a PSF convolution: e.g., motion artifacts from sample vibration by the stage drift. In all the cases, our reference-free deep-learning-based super-...
Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng(曾晋哲), Linfeng Zhang(张林峰), Han Wang(王涵), and Weinan E(鄂维南), DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models, Computer Physics Communications, 253:107206, 2020. pdf ...
‘Intuitive physics’ enables our pragmatic engagement with the physical world and forms a key component of ‘common sense’ aspects of thought. Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to ev
1). Deep learning-based dose prediction studies have been reported on tumors centrally located in the body, such as rectal [24] and prostate [25, 26] cancer tumors. In the case of breast cancer, the target anatomical position is close to the body outside the area and the left lung, ...