The wave equation is a very important formula that is often used to help us describe waves in more detail. It should be noted that some particular waves have their own specific speeds. The speed of ...
Abstract: Imaging using S-wave seismic data based on elastic media holds the potential to address certain critical issues that conventional P-wave imaging cannot resolve, thereby offering new ...
Abstract: We propose a Physics-Informed Neural Network (PINN) approach to model elastic wave propagation in 2D plate-like structures with potential fractures. The goal is to retrieve the ...
A collection of interactive simulations built with SvelteKit, exploring steady-state dynamics in competitive and chemical systems. Originally a Python Arcade project, now fully rewritten as a modern ...
Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...
The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...
Rheology, high-speed imaging, and velocimetry combined to characterize how some roller-based nozzles perform so well with non-Newtonian fluids, such as whey protein. Most conventional spray systems ...
Generates synthetic tasks where a ball bounces off walls in a bounded area. The goal is to predict and animate the complete trajectory path based on initial position and velocity, stopping after a ...
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