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E. Mušeljić, A. Reinbacher-Köstinger, M. Kaltenbacher
1 24. 10. 2022.

Solving the electrostatic Laplace's equation with a parameterizable physics informed neural network

In this work, we present an approach for training parametrized physics informed neural networks to solve PDEs in a self supervised fashion, which means that no labeled input-output data is needed to train the neural network. The main contribution of this work is the achievement of a model with parameterizable boundary condition functions. This means that no retraining is needed to produce correct results for changing boundary conditions.

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