D-CIPHER: Discovery of Closed-form Partial Differential Equations
Authors: Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a series of synthetic experiments to show how well D-CIPHER is able to discover some well-known differential equations |
| Researcher Affiliation | Collaboration | Krzysztof Kacprzyk University of Cambridge kk751@cam.ac.uk Zhaozhi Qian University of Cambridge zq224@cam.ac.uk Mihaela van der Schaar University of Cambridge, The Alan Turing Institute mv472@cam.ac.uk |
| Pseudocode | Yes | The pseudocode of D-CIPHER is presented in Algorithm 1. |
| Open Source Code | Yes | All experiment code can be found at https://github.com/krzysztof-kacprzyk/D-CIPHER |
| Open Datasets | No | The paper states that fields for experiments were "generated by solving the equation" and observed fields by "sampling (t, x) ... and adding Gaussian noise" (Appendix E.5). This indicates synthetic data generation rather than the use of a publicly accessible, pre-existing dataset with a concrete link, DOI, or formal citation. |
| Dataset Splits | No | The paper describes how synthetic data is generated for experiments, mentioning "sampling grid" and "number of samples" (Appendix E.5, E.6). However, it does not specify traditional dataset splits such as exact percentages or sample counts for training, validation, and testing partitions of a fixed dataset. |
| Hardware Specification | Yes | The time is measured on a single computer with an Intel Core i5-6500 CPU (4 cores) and 16GB of RAM. |
| Software Dependencies | No | Table 7 lists "Software used and their licenses" (e.g., gplearn, cvxopt, sympy, scikit-learn, numpy, scipy, python, pysindy) but does not provide specific version numbers for these software components. For example, it lists "scikit-learn BSD 3-Clause" rather than "scikit-learn 1.2.3". |
| Experiment Setup | Yes | Table 6: Hyperparameters used in gplearn |