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Neural Networks tend to overfit. One way to overcome this problem is by considering a sequence of optimization problems, where we modify the loss function so that we minimize the distance to the previous solution in every step.
Implementation of two phase field approaches for the surface reconstruction problem and shape space learning. One based of the Modica-Mortola theorem and the other based on Ambrosio-Tortorelli