Summary of Sobolev Neural Network with Residual Weighting As a Surrogate in Linear and Non-linear Mechanics, by A.o.m. Kilicsoy et al.
Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics
by A.O.M. Kilicsoy, J. Liedmann, M.A. Valdebenito, F.-J. Barthold, M.G.R. Faes
First submitted to arxiv on: 23 Jul 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 This paper investigates the application of sensitivity information to improve the training process of artificial neural networks for complex nonlinear systems. By incorporating partial derivatives w.r.t. inputs (Sobolev training), the authors expand the traditional loss function with additional terms, enhancing training convergence and reducing generalisation error. The proposed approach is demonstrated on two examples: linear and non-linear material behavior. To optimize residual weights, an adaptive scheme adjusts the scaling of response and sensitivities data during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special math to help artificial intelligence learn from complex systems. It’s like giving a hint to the AI about what it should focus on. The authors test this idea with two types of materials: one that behaves in a simple way, and another that is more complicated. They show that by using these hints, the AI can learn faster and make better predictions. |
Keywords
* Artificial intelligence * Loss function
