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..
Automatically Learning Hybrid Digital Twins of Dynamical Systems
Authors: Samuel Holt, Tennison Liu, Mihaela van der Schaar
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results reveal that HDTwin Gen produces generalizable, sample-efficient, and evolvable models, significantly advancing DTs efficacy in real-world applications. |
| Researcher Affiliation | Academia | Samuel Holt , Tennison Liu & Mihaela van der Schaar DAMTP, University of Cambridge Cambridge, UK EMAIL |
| Pseudocode | Yes | An overview of our method is presented in Figure 1, with pseudocode in Appendix E.1. |
| Open Source Code | Yes | 2Code is available at https://github.com/samholt/HDTwin Gen. |
| Open Datasets | Yes | We evaluate against six real-world complex system datasets; where each dataset is either a real-world dataset or has been sampled from an accurate simulator designed by human experts. Three are derived from a state-of-the-art biomedical Pharmacokinetic-Pharmacodynamic (PKPD) model of lung cancer tumor growth, used to simulate the combined effects of chemotherapy and radiotherapy in lung cancer [61] (Equation (2))... We also compare against an accurate and complex COVID-19 epidemic agent-based simulator (COVID-19) [65]... Furthermore, we compare against an ecological model of a microcosm of algae, flagellate, and rotifer populations (Plankton Microcosm) replicating an experimental three-species prey-predator system [66]. Moreover, we also compare against a real-world dataset of hare and lynx populations (Hare-Lynx), replicating predator-prey dynamics [67]. |
| Dataset Splits | Yes | Here, the outer objective measures the generalization performance, empirically measured on the validation set Lval, while the inner objective measures the fitting error, as evaluated on the training set Ltrain. |
| Hardware Specification | Yes | We perform all experiments and training using a single Intel Core i9-12900K CPU @ 3.20GHz, 64GB RAM with an Nvidia RTX3090 GPU 24GB. |
| Software Dependencies | Yes | Specifically, we find a top-K, where K = 16 is sufficient. Additionally, we use the LLM of GPT4-1106-Preview, with a temperature of 0.7. |
| Experiment Setup | Yes | Specifically, we train the model on the training dataset, using the standard MSE loss Equation (5), optimizing using the Adam optimizer [32]. We use the same optimizer hyperparameters as the black-box neural network method, that of a learning rate of 0.01, with a batch size of 1,000 and early stopping with a patience of 20, and train it for 2,000 epochs to ensure it converges, to ensure fair comparison. |