Learning Macroscopic Dynamics from Partial Microscopic Observations
Authors: Mengyi Chen, Qianxiao Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 chenmengyi@u.nus.edu, qianxiao@nus.edu.sg |
| 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, λ 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 λcond 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. |