In recent years,neural networks have become an increasingly powerful tool in scientific computing.The universal approximation theorem asserts that a neural network may be constructed to approximate any given continuous function at desired accuracy.The backpropagation algorithm further allows efficient optimization of the parameters in training a neural network.Powered by GPU’s,effective computations for scientific and engineering problems are thereby enabled.In addition,we show that finite element shape functions may also be approximated by neural networks.
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