GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver
Authors: David John, Vincent Heuveline, Michael Schober
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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. |