Networks are systems comprised of two or more connected devices, biological organisms or other components, which typically ...
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 ...
Dr. Sai Nethra Betgeri has developed a new artificial intelligence method that combines machine learning with physics to solve one of the most fundamental equations in science — the advection equation ...
Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
Abstract: Physics-informed neural networks (PINNs) have great potential for flexibility and effectiveness in forward modeling and inversion of seismic waves. However, coordinate-based neural networks ...
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...
physics_informed_neural_network/ ├── app/ # FastAPI application │ ├── __init__.py │ ├── api/ # API endpoints │ │ ├── __init__.py ...
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