Automatically Learning Hybrid Digital Twins of Dynamical Systems
Authors: Samuel Holt, Tennison Liu, Mihaela van der Schaar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 {sih31, tl522, mv472}@cam.ac.uk |
| 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. |