Corpus ID: 237513493

Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs

@article{Wandel2021SplinePINNAP,
  title={Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs},
  author={Nils Wandel and Michael Weinmann and Michael Neidlin and R. Klein},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.07143}
}
Partial Differential Equations (PDEs) are notoriously difficult to solve. In general, closed-form solutions are not available and numerical approximation schemes are computationally expensive. In this paper, we propose to approach the solution of PDEs based on a novel technique that combines the advantages of two recently emerging machine learning based approaches. First, physics-informed neural networks (PINNs) learn continuous solutions of PDEs and can be trained with little to no ground… Expand

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