Mathematical modelIntelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and has demo
From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? Environ. Sci. Technol. 55, 2357–2368 (2021). Article Google Scholar Zhi, W., Ouyang, W., Shen, C. & Li, L. Temperature outweighs light and flow as the ...
In this paper, we demonstrate physical deep learning by augmenting the DFA algorithm. In the augmented DFA, we replace the differential of physical nonlinear activationf’(a) in the standard DFA with arbitrary nonlinearityg(a) and show that the performance is robust to the choice ofg(a). Owi...
The authors introduce deep learning-optimized diffractive waveguides enabling various capabilities such as spatial and spectral mode filtering, mode splitting, and mode-specific polarization control, presenting their versatility and scalability. Yuntian Wang ...
While there is a variety of techniques for building a ROM, this example builds an LSTM-ROM (a type of ROM that leverages an LSTM network) and uses it in a Simulink model as part of a Deep Learning Stateful Predict block. To train the LSTM network, the example uses the original model ...
NeuroPhysNet addresses these challenges by seamlessly integrating deep learning techniques with physics-based modeling, offering a robust framework that leverages both data-driven insights and domain-specific knowledge. The architecture comprises three main components: a Deep Neural Network (DNN) module for...
The Bi-LSTM model is constructed to classify a worker’s physical load conditions through an ankle-worn WIMU. The cross validation process has been adopted to test the classification performance of the Bi-LSTM model, similar to previous research utilizing deep learning algorithms [69], [70], [...
Han presented a data-driven model to solve the PDEs by neural networks to approximate the gradient of the solution [19], whose gradient acting is similar to the policy function in the deep reinforcement learning. More pieces of literature can be found in these references [20], [21], [22]...
Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry Life activities are inextricably linked to the regulation of trace copper ions. Here, the authors report a deep learning-assisted electrochemical sensor for single-atom detection of copper...
63. An AF2Complex run of ~7000 pairs of proteins using 923 nodes required about 2 hours in wall clock time. Each node has 6 Nvidia 16 GB V100 GPUs. For an individual target of fewer than 1000 residues, models may be obtained within 20 min for each deep learning model using “...