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About 2023-Physics Informed Neural Networks - A Case Study For Gas Transport Problems
This document summarizes a journal article that evaluates different physics-informed neural network approaches for modeling gas transport problems. The article reviews variants of PINN that can balance loss functions and compares their performance on gas transport applications. It finds that the original PINN approach with constant weights in the loss function performs best. Loss balancing methods developed for other differential equations provided no benefits for gas problems. The control volume PINN formulation was also not better than the initial formulation. L-BFGS was the best optimization strategy.
Detailed Information
| Author: | ['maycvc'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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