In a series of short videos, Brown University engineering professor Roberto Zenit explores the physics of soccer as the World ...
Brown University researchers have developed a new artificial intelligence method for predicting the rate at which materials ...
Abstract: Shape control of deformable linear objects (DLOs) is a major challenge in robotics due to their high-dimensional, nonlinear dynamics and sensitivity to boundary conditions. Existing ...
Abstract: In this paper, a method for inferring the motion intentions of a neighboring vehicle ahead of an ego vehicle using a physics-informed deep neural network-based open-set classification ...
Accurate joint kinematics estimation is essential for understanding human movement and supporting biomechanical applications. Although optical motion capture systems are accurate, their high cost, ...
Short video shows the neural network training results and reproduction of flocking from real-world data. Credit: Cell Reports Physical Science Learning local rules with physics-informed AI To address ...
A case study in aerospace manufacturing provides an overview of how physics-informed digital twin systems transform robotics processes—from adaptive process planning and real-time process monitoring ...
Resilient energy systems depend on reliable batteries. The lithium-ion (Li-ion) batteries powering our world must endure the steady strain of time, charge cycles, and environmental conditions that ...
This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...
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