Robust Symbolic Regression for Dynamical System Identification
Authors: Ramzi Dakhmouche, Ivan Lunati, Hossein Gorji
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The numerical experiments demonstrate the competitive performance of SDFL in comparison to the state-of-the-art. We illustrate the performance of the proposed scheme on the prototypical problem of Kuramoto networks and a standard benchmark of single-cell RNA sequence trajectory data. |
| Researcher Affiliation | Academia | Ramzi Dakhmouche EMAIL Institute of Mathematics, EPFL Laboratory for Computational Engineering, EMPA Ivan Lunati EMAIL Laboratory for Computational Engineering, EMPA Hossein Gorji EMAIL Laboratory for Computational Engineering, EMPA |
| Pseudocode | Yes | Algorithm 1 Symbolic Distribution Flow Learner Algorithm 2 Symbolic Distribution Flow Learner [extended description] |
| Open Source Code | Yes | Additionally, to foster reproducibility, a Python implementation of SDFL has been made public at https://github.com/Ramzisofo/SDFL. |
| Open Datasets | Yes | Then, we conduct an evaluation on a real-world dataset of embryoid stem cell trajectories (Moon et al., 2019). |
| Dataset Splits | No | The paper mentions 'unseen data' and 'Training Sample Size (TSS) per snapshot' with specific sample sizes for evaluation (e.g., m=50), but does not provide specific details on how the overall datasets (Kuramoto, single-cell RNA-seq) were split into training, validation, or test sets using percentages, counts, or specific methodologies. |
| Hardware Specification | Yes | For a fair comparison, all the reported running times are obtained on an Intel(R) Core(TM) i7-7500U CPU. |
| Software Dependencies | No | The paper mentions a "Python implementation of SDFL" and "publicly available implementations of JKOnet and Trajectory Net" but does not specify any version numbers for Python or any specific libraries/solvers used. |
| Experiment Setup | Yes | For the implementation of SDFL, we set the building operations consisting of {+, , , , cos, sin, exp} with a maximum of L = 20 operations per expression, with a number of episodes of 500 to 1000. For the recovery of the Kuramoto system, we use 15 snapshots with time-stamps ti = 2i for 1 i 15, and we set K = 1/3. ... For JKOnet, we use a small regularization parameter ε = 0.001 to make its target closer to the Wasserstein distance. |