Deep Neural Networks Rapidly Predict the Performance of Touch-Mode MEMS Pressure Sensors
Researchers trained a deep multilayer perceptron on finite element and numerical data to predict the performance of a rectangular touch-mode ...
Researchers trained a deep multilayer perceptron on finite element and numerical data to predict the performance of a rectangular touch-mode ...
Hydrogen is often described as the fuel of the future, but getting it out of storage quickly remains one of ...
Researchers have proposed a Surrogate Human Digital Twin that uses active inference and free-energy minimization to model embodied human perception ...
Researchers combined an artificial neural network surrogate model with an enhanced NSGA-III algorithm to optimize a 20-tonne wave compensation Stewart ...
Researchers have built a microgrid scheduling system that pairs online electric vehicle battery state estimation with a neural network surrogate, ...
A new machine learning framework trained on roughly 434,000 nonlinear earthquake simulations reveals that taller steel buildings need proportionally fewer ...
A new study shows that a small deep neural network trained on simulations of a classic biophysical model can accurately ...
Researchers used a surrogate modeling approach with 46 designed experiments to optimize a rocket thrust chamber, achieving gains of up ...
Researchers in South Korea used Kriging surrogate modeling and multi-objective global optimization to design multiple-harmonic individual blade pitch control schedules ...
A new convolutional neural network surrogate with leaky ReLU activation dramatically accelerates and sharpens the identification of hidden groundwater pollution ...
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© 2025 Scienmag - Science Magazine