Spectral Learning of Dynamic Systems from Nonequilibrium Data
Authors: Hao Wu, Frank Noe
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, some numerical experiments are provided to demonstrate the capability of the proposed methods.In this section, we evaluate our algorithms on two diffusion processes and the molecular dynamics of alanine dipeptide, and compare them to several alternatives. |
| Researcher Affiliation | Academia | Hao Wu and Frank Noé Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 6, 14195 Berlin {hao.wu,frank.noe}@fu-berlin.de |
| Pseudocode | Yes | Algorithm 1 General procedure for spectral learning of OOMs; Algorithm 2 Procedure for learning binless equilibrium OOMs |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper describes the data generation process for 'Brownian dynamics' and 'alanine dipeptide' simulations but does not provide specific access information (links, DOIs, repositories, or formal citations with author/year for public datasets) to the datasets used in the experiments. |
| Dataset Splits | No | The paper describes simulation lengths and number of trajectories, and mentions error bars from '30 independent experiments', but it does not specify explicit train/validation/test dataset splits, percentages, or cross-validation methods. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud computing resources) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies, libraries, or packages with their version numbers that were used for the experiments or implementation. |
| Experiment Setup | Yes | The detailed settings of simulations and algorithms are provided in Appendix B. |