Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

Learn how to build your own GPT-style AI model with this step-by-step guide. Demystify large language models and unlock their ...

Inverse problem mainly used to approximate physical parameters of material. Finite element method will be combined with artificial neural network using back propagation algorithm to solve differential ...

Next, unlike conventional physics-informed neural networks that only utilize macroscopic physical information, we constrain the training of the neural network by using dynamic metabolic flux analysis ...

This paper introduces a novel approach for predicting gasoline engine torque using a neural network model based on the Levenberg-Marquardt algorithm (LMA). The model generates an engine torque map by ...

Reliable 5G positioning is vital for smart cities, driverless cars, and next-gen mobile services. Yet in dense urban landscapes, high-rise buildings often distort signals, leading to major ...

i want use this network to train my dataset， but i don't konw how to use this network. #32 ...

What makes this development especially revolutionary in battery research is the integration of physics-informed principles into neural networks. Traditional neural networks are data-driven models that ...