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Solving differential equations with unknown constitutive relations as recurrent neural networks
[article]
2017
arXiv
pre-print
We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term. We assume that the measurements (time series) of state variables are partially available, and we use recurrent neural network to "learn" the reaction rate from this data. This is achieved by including a discretized ordinary differential equations as part of a recurrent neural network training problem. We extend TensorFlow's recurrent neural network architecture to create a simple
arXiv:1710.02242v1
fatcat:2kuqcgfanvax5jt6bz4cmqjjpi