Abstract:We will review physics-informed neural network and summarize available extensions for applications in computational mechanics and beyond. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. The universal approximation theorem of operators is suggestive of the potential of NNs in learning fro...
Abstract: Deep learning has enjoyed huge empirical success in recent years. Although training a deep neural network is a highly nonconvex optimization problem,simple (stochastic) gradient methods are able to produce good solutions that minimize the training error, and more surprisingly, can generalize well to out-of sample data, even when the number of parameters is significantly larger than t...
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