Researchers have uncovered a fast-acting brain network that may determine how effectively deep brain stimulation improves Parkinson’s symptoms. Parkinson’s disease can make everyday movements slow, ...
Abstract: The abundant knowledge of data and physics models can be simultaneously utilized in learning-based modeling, prediction, and control methods, which makes the balance between model efficiency ...
ABSTRACT: Rubber is widely used in automotive vibration isolation systems due to its excellent mechanical properties and durability. However, elastomeric support components tend to experience ...
Introduction: Accurate joint kinematics estimation is essential for understanding human movement and supporting biomechanical applications. Although optical motion capture systems are accurate, their ...
Abstract: Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able ...
πMRF (Physics-informed implicit neural MRF) is a physics-informed unsupervised framework for accurate quantitative parameter mapping via global spatio-temporal inversion. piMRF/ ├── main.py # Runnable ...
In our increasingly electrified world, supercapacitors have emerged as critical components in transportation and renewable energy systems, prized for their remarkable power density, cycling stability, ...
A neural network is a machine learning model originally inspired by how the human brain works (Courtesy: Shutterstock/Jackie Niam) Precision measurements of theoretical parameters are a core element ...
One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...
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