The end effector of a robot manipulator needs to be accurately positioned in the workspace when it is used for automated applications. Manipulators are driven using the torque inputs supplied to the ...

Neuromorphic computing, as a novel approach to processing information by mimicking biological neural networks, has gradually demonstrated significant ...

This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...

Floods are some of the most devastating natural disasters communities in the United States face, causing billions of dollars ...

Using machine learning and math, a BYU student improved a key tool firefighters rely on during wildfire season ...

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

We propose a neural network (NN) approach that yields approximate solutions for high-dimensional optimal control (OC) problems and demonstrate its effectiveness using examples from multiagent path ...

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 ...