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Maximilian Stadler

2 accepted papers

2023

Geometry-Informed Neural Operator for Large-Scale 3D PDEs

NeurIPS 2023poster

We propose the geometry-informed neural operator (GINO), a highly efficient approach for learning the solution operator of large-scale partial differential equations with varying geometries. GINO uses a signed distance function (SDF) representation of the input shape and neural operators based on gr…

Cited by 106SourcePDFScholar
2021

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

NeurIPS 2021poster

The interdependence between nodes in graphs is key to improve class prediction on nodes, utilized in approaches like Label Probagation (LP) or in Graph Neural Networks (GNNs). Nonetheless, uncertainty estimation for non-independent node-level predictions is under-explored. In this work, we explore…