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teaching:ss2024:dl4science:summaries [2024/08/06 16:33] Karnik Ram |
teaching:ss2024:dl4science:summaries [2024/08/06 16:33] (current) Karnik Ram |
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| However, one thing that users need to be very careful about the weighting of the added loss functions, which can result in extensive testing or hyper parameter tuning. Sometimes the inference time can be quite slow, and it might be better to use a numerical solver. | However, one thing that users need to be very careful about the weighting of the added loss functions, which can result in extensive testing or hyper parameter tuning. Sometimes the inference time can be quite slow, and it might be better to use a numerical solver. | ||
| All in all the paper introduced a simple and very effective way to enforce physical properties into the approximations of a neural network which forces the network to not hallucinate as much and come up with realistic solutions. Thereby the authors made a significant contributions towards the field of deep learning for natural sciences. | All in all the paper introduced a simple and very effective way to enforce physical properties into the approximations of a neural network which forces the network to not hallucinate as much and come up with realistic solutions. Thereby the authors made a significant contributions towards the field of deep learning for natural sciences. | ||
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| + | Alternate title: How to make your neural network start caring about physics and stop hallucinating. | ||
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