Our aim was to lower the entry-barrier for Engineering AI, to get the power of modern deep learning into classical engineering.

Engineering and simulation data such as what is used for example in Computational Fluid Dynamics is of high-cardinality. Single examples can be up point clouds with multiple millions of points, requiring distinct approaches to models architectures and data loading that aren't served well by other frameworks. Noether comes with optimized architectures and components that make it easier to train models on simulation data.

We also provide a tutorial that runs you through all the steps involved in training a surrogate model: https://github.com/Emmi-AI/noether/blob/main/tutorial/README...

Comments URL: https://news.ycombinator.com/item?id=46798700

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I am not convinced it is a low hanging fruit, it's something that is super easy for humans but not trivial for machines, but you are right that it is being neglected by many. I work for speechmatics.com and we spent a significant amoutn of effort over the years on it. We now believe we have the world's best real-time speaker diarization system, you should give it a try.