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..
Learning Macroscopic Dynamics from Partial Microscopic Observations
Authors: Mengyi Chen, Qianxiao Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the accuracy, force computation efficiency, and robustness of our method on learning macroscopic closure models from a variety of microscopic systems, including those modeled by partial differential equations or molecular dynamics simulations. |
| Researcher Affiliation | Academia | Mengyi Chen1, Qianxiao Li1, 2 Department of Mathematics, National University of Singapore1, Institute for Functional Intelligent Materials, National University of Singapore2 EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Data generation. and Algorithm 2 Training procedure. |
| Open Source Code | Yes | Our code is available at https://github.com/MLDS-NUS/Learn-Partial.git. |
| Open Datasets | No | The paper describes generating data from simulated systems (Predator-Prey, Lennard-Jones, Allen-Cahn) but does not provide access information or citations for a publicly available, pre-existing dataset. It states 'We choose D to be the trajectory distribution of the state x.'. |
| Dataset Splits | No | The paper discusses 'training data' and 'test dataset' but does not explicitly specify distinct 'validation' splits or percentages. |
| Hardware Specification | Yes | All the experiments are run on a single NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions LAMMPS for simulations but does not provide a specific version number. Other software dependencies like deep learning frameworks are implied but not explicitly versioned. |
| Experiment Setup | Yes | In our experiment, we choose a 3, b 0.4, ฮป 0. The microscopic equation is solved with a uniform time step t 0.01 from t 0 to t 30 using the Euler method. and ฮปcond is a hyperparameter to adjust the ratio of Lcond and is chosen to be quite small in our experiments, e.g., 10 5 or 10 6. and The integration step is 0.001 and each trajectory is integrated for 250 steps. We sample the initial temperature randomly from r0.5, 1.5s. |