Summary of Revising the Structure Of Recurrent Neural Networks to Eliminate Numerical Derivatives in Forming Physics Informed Loss Terms with Respect to Time, by Mahyar Jahani-nasab et al.
Revising the Structure of Recurrent Neural Networks to Eliminate Numerical Derivatives in Forming Physics Informed Loss Terms with Respect to Time
by Mahyar Jahani-nasab, Mohamad Ali Bijarchi
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Mutual Interval RNN (MI-RNN) model enables the prediction of each block over a time interval, allowing for the calculation of derivatives using backpropagation. This is achieved by overlapping time intervals and defining a mutual loss function between blocks, as well as employing conditional hidden states to ensure unique solutions for each block. The forget factor controls the influence of conditional hidden states on subsequent predictions. MI-RNN demonstrates improved accuracy in solving partial differential equations (PDEs) compared to traditional RNN models with numerical derivatives. For instance, it achieves one order of magnitude less relative error than an RNN model when applied to unsteady heat conduction in an irregular domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study proposes a new way to solve unsteady partial differential equations using recurrent neural networks. It’s like trying to find the right answer by looking at what happened before and after. The researchers created a special kind of RNN that can look at a longer period of time, not just one block at a time. This helps the model learn more accurately by giving it more information to work with. They tested this new approach on three different problems and found that it was much better than other methods. For example, it got an answer that was 10 times closer to the correct answer than another method. |
Keywords
» Artificial intelligence » Backpropagation » Loss function » Rnn
