dynoNet: A neural network architecture for learning dynamical systems This repository contains the Python code to reproduce the results of the paper dynoNet: A neural network architecture for learning dynamical systems by Marco Forgione and Dario Piga. In this work, we introduce the linear dynamical...
In recent years, advances in integrating DFT calculations with machine learning have led to the development of neural network potentials (NNPs)20, which learn the potential energy landscape directly from reference DFT datasets21. They maintain near-DFT accuracy while affording atomic resolution at larg...
Then, the prismatic models of buildings are generated by analyzing the eave lines. The parametric models of individual roofs are also reconstructed using the predicted ridge and hip lines. The experiments show that, even in the presence of noises in height values, the proposed method performs ...
deep learningmultifidelity neural networkThe development and calibration of soil models under the framework of plasticity is notoriously challenging given the prismatic features in soil's shear behaviors. Data‐driven deep neural networks (DNNs) offer an alternative approach to this formidable task. ...
In recent years, advances in integrating DFT calculations with machine learning have led to the development of neural network potentials (NNPs)20, which learn the potential energy landscape directly from reference DFT datasets21. They maintain near-DFT accuracy while affording atomic resolution at larg...
This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive ...
“General Atomics Prismatic Modular High Temperature Gas Cooled Reactor,” General Atomics; https://aris.iaea.org/PDF/PrismaticHTR.pdf(open in a new window). (Open in a new window)Google Scholar J. COLLINS, “Next Generation Nuclear Plant Project Technology Development Roadmaps: The Technical ...
Moreover, a relationship was discovered between the concentration of the extract as reductant agent and the predominant type of GNPs: more triangular and prismatic NPs are formed than hexagonal and spherical ones at lower concentrations of the extract used86. According to Abirami et al.87, ...
and mass loss rate of the prismatic specimen were selected as input, and the compressive bearing capacity of the specimen was selected as output. The predictive model was trained using the autonomous learning ability of a convolutional neural network. Convolution kernel k with sixteen 3×1 parameter...
The process of determining connectivity strength within the functional network is the third step. Two distinct datasets of iEEG recordings from 59 patients with drug-resistant epilepsy were examined in the experimental procedures. Connectivity strength exhibited a statistically significant difference (p < ...