Support forward-mode AD #1903
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Support forward-mode AD #1903
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Update example of heat equation (lululxvi#706)
Add document for Lorenz inverse with exogenous input (lululxvi#709)
OperatorPredictor supports backends tensorflow.compat.v1, tensorflow,…
… method of Lr decay in Pytorch
lululxvi
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lululxvi
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lululxvi
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lululxvi
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lululxvi
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lululxvi
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lululxvi
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lululxvi
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deepxde/data/pde_operator.py
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| error = bc.error( | ||
| error_f = [fi[:, bcs_start[-1] :] for fi in f] | ||
| # Each error has the shape (N1, ~N2) | ||
| losses = [loss_fn(bkd.zeros_like(error), error) for error in error_f] |
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The same thing here. For example, if loss_fn is sum, then the code is not correct. Need to first compute the loss for each function, then average over functions.
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Also this part. What is the speed of the current code vs the correct code?
lululxvi
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lululxvi
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Support forward-mode AD and fix a bug when multi-output is needed