这篇是从上一篇All-optical machine learning using diffractive deep neural networks延伸出来的一种训练衍射神经网络的方法,十分巧妙。 论文地址如下,来自 Photonics Research:https://www.osapublishing.org…
In situ optical backpropagation training of diffractive optical neural networks:publisher's note opticalnetworksneuralThis publisher's note corrects the authors'affiliations in Photon.Res.8,940(2020).TIANKUANG ZHOULU FANGTAO YANJIAMIN WUYIPENG LIJINGTAO FANHUAQIANG WUXING LINQIONGHAI DAI光子学研究:英文...
training of an artificial neural network is a crucial step in its application. However, currently on the integrated photonics platform there is no efficient protocol for the training of these networks. In this work, we introduce a method that enables highly efficient,in situtraining of a photonic...
DVC mechanical regularization applied to crack propagation during a wedge splitting test on a concrete specimen. Data courtesy of P. Carrara, ETHZ. A. Mishra, P. Carrara, M. Griffa, L. De Lorenzis, (2023) Fracture in concrete: in situ X-ray tomography tests, digital volume correlation, an...
Taking gold nanospheres as an example, we contrasted the inverse design method of gold nanoparticles based on least square method and neural network for error back propagation (BP) training. The calculation results indicated that the inverse design method based on deep learning is more adaptable and...
The weights are updated during the training of an ANN, usually via a process known as back propagation, to minimise the difference between a prediction and target (Gardner & Dorling,1998). ANNs span a large family of different algorithms, including CNNs (Soni et al.,2021). However, the pr...
the cross-section of oxide islands on the copper. Please note that the oxide island already started to get reduced in the central part at the moment when the in situ SEM experiment was quenched. Red and green arrows indicate the propagation of the oxidation and reduction fronts. c Development...
The in situ online training was composed of two stages: feedforward inference and feedback weight update. The multilayer inference was performed layer by layer sequentially. The input voltage vector to the first layer was a feature vector from the dataset, while the input vector for the subsequen...
In-situ Monitoring MethodDevices UsedMonitoring ObjectiveReference Powder Bed Fusion (PBF) Laser Thermography IR Camera; Pyrometer Device integration [123] Visual Imaging; Thermography Optical cameras; Pyrometer Device integration [124], [125], [126], [127] Visual Imaging Optical cameras Device in...
Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identifica