Convolutional Neural Operators for robust and accurate learning of PDEs
Authors: Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, Emmanuel de Bézenac
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | CNOs are tested on a novel suite of benchmarks, encompassing a diverse set of PDEs with possibly multi-scale solutions and are observed to significantly outperform baselines, paving the way for an alternative framework for robust and accurate operator learning. |
| Researcher Affiliation | Academia | 1 Seminar for Applied Mathematics, ETH, Zurich, Switzerland 2 ETH AI Center, Zurich, Switzerland 3 Delft University of Technology, Netherlands |
| Pseudocode | No | The paper describes the architecture and components but does not provide pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1The code can be found at https://github.com/bogdanraonic3/Convolutional Neural Operator |
| Open Datasets | Yes | access to training and test data is readily available for rapid prototyping and reproducibility |
| Dataset Splits | Yes | For each such hyperparameter configuration, the corresponding models are trained on the benchmark and the configuration with smallest validation error is selected and the resulting test errors are reported |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models) used for experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. |
| Experiment Setup | No | We provide a detailed description of the implementation of CNO and the training (and test) protocol for CNO as well as all the baselines in SM C.1. |