Papers › Machine Learning for semi linear PDEs
Machine Learning for semi linear PDEs
Quentin Chan-Wai-Nam, Joseph Mikael, Xavier Warin
Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations. These algorithms are compared to a new one that solves a fixed point problem by using deep learning techniques. This new algorithm appears to be competitive in terms of accuracy with the best existing algorithms.
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