Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |