Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Abstract: Based on unsupervised physics-informed neural network (PINN) framework, a two-dimensional inverse-design method for antenna superstrate is proposed, which can simultaneously realize the ...
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 ...
In the context of the rapid development of computer hardware and the continuous improvement of the artificial intelligence and deep learning theory, aiming at the traditional numerical solution method ...
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 ...
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 ...
Accessing ocean velocity data is critical to improving our understanding of ocean dynamics, which affects our prediction capabilities for a range of services that the ocean provides. Because ocean ...
MKDPINN is a novel approach for Remaining Useful Life (RUL) prediction, combining meta-learning, knowledge discovery, and Physics-Informed Neural Networks (PINNs). This repository contains the code ...
physics_informed_neural_network/ ├── app/ # FastAPI application │ ├── __init__.py │ ├── api/ # API endpoints │ │ ├── __init__.py ...
Robbie has been an avid gamer for well over 20 years. During that time, he's watched countless franchises rise and fall. He's a big RPG fan but dabbles in a little bit of everything. Writing about ...
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