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Tobias Rohner

2 accepted papers

2024

Poseidon: Efficient Foundation Models for PDEs

NeurIPS 2024poster

We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enable continuous-in-time evaluations. A novel training strategy leveraging the semi-group property of time-dependent PDEs t…

2023

Convolutional Neural Operators for robust and accurate learning of PDEs

NeurIPS 2023poster

Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs. Here, we present novel adaptations for convolutional n…