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 ...
Omarkhan Samarkanov, Masoud Riazi School of Mining and Geosciences Presented at: 6th EAGE Global Energy Transition Conference & Exhibition (GET ...
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 ...
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 ...
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 ...
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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