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..
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver
Authors: David John, Vincent Heuveline, Michael Schober
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments and comparison to other solvers are presented in Section 5. |
| Researcher Affiliation | Collaboration | 1Corporate Research, Robert Bosch GmbH, Renningen, Germany 2Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany 3Bosch Center for Artificial Intelligence, Renningen, Germany. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Matlab code is available at https://github.com/boschresearch/GOODE |
| Open Datasets | Yes | The testset can be obtained from Mazzia (2014). |
| Dataset Splits | No | The paper refers to a 'testset' which is a collection of problems, not a single dataset with defined train/validation/test splits. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | No | The paper states, 'We have implemented our method in Matlab', but does not provide a specific version number for Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | If not stated otherwise, we will use the following default setting to obtain the results: squared exponential kernel, equidistant mesh RN including the boundary points, with N = 31, grid search for λ [1.5h, 15h] with M = 40 logarithmic spaced grid points and ε = 0.1 for all the problems. |