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> In physics and engineering, the challenge is to make mathematical models of the world that accurately predict how it works. Once we've done that we can declar
by srean 7y ago
> In physics and engineering, the challenge is to make mathematical models of the world that accurately predict how it works. Once we've done that we can declare success.
With enough epicycles one can make the heliocentric model as accurate as one wants as far as forecasting positions of planets. A system of DNN sequence models will do just as good, if not better.
Since we regard Einstein highly (at least more highly than an undergrad who can train a TensorFlow model on planetary position data), it makes me think there is more to physics than having a mathematical model with good predictive accuracy.
- shoyer 7y agoIt's actually extraordinarily difficult to get a DNN model to accurately predict planetary motion over arbitrary time horizons. RNNs have a tendency to either decay or blow-up. The only way I know to make neural nets work well for this problem is to build in lots of physics into the model architecture, e.g., conservation of energy: https://arxiv.org/abs/1906.01563 https://arxiv.org/abs/1906.01563