Neural Network Surrogate Models MHD Nanofluid Flow Between Porous Disks
Researchers trained Levenberg–Marquardt neural networks on bvp4c solutions to rapidly predict coupled MHD nanofluid transport between porous disks.
Researchers trained Levenberg–Marquardt neural networks on bvp4c solutions to rapidly predict coupled MHD nanofluid transport between porous disks.
A new human-inspired SELDD neural network cuts angular error from 36.9 to 25.7 degrees and distance error from 0.33 to ...
A BERT-based framework combining about 12,000 consumer reviews with statistical data from 15 regional nodes identified Harbin as the absolute ...
Researchers have trained a two-hidden-layer neural network with Levenberg–Marquardt backpropagation to accurately approximate the trajectories of a nonlinear mathematical model ...
Researchers used adaptive genetic and particle swarm-optimized neural networks to convert oil-based drilling cuttings into Na-X zeolite with record ammonia ...
A new study of a 40-meter highway slope in Southwest China shows how saturated clay seams trigger stepped sliding failure, ...
A Kansas State University team used a YOLOv11 neural network to track thousands of condensing droplets on laser-patterned aluminum, revealing ...
Deep learning models trained on noisy acoustic signals can classify and localize natural gas pipeline leaks with over 99% accuracy, ...
Researchers combined ballistic experiments, finite element modeling, and a neural network to predict how PEEK plates fail under oblique high-speed ...
Researchers have trained a deep neural network on 130,000 simulated samples to predict the reflection coefficients of an eight-port terahertz ...
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine