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 [1].
Machine learning structure preserving brackets for forecasting irreversible processes
Authors: Kookjin Lee, Nathaniel Trask, Panos Stinis
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we assess the performance of the three parameterizations of the ODE dynamics which apply progressively more stringent priors. We implement the algorithms in PYTHON 3.6.5, NUMPY 1.16.2, and PYTORCH 1.7.1 [48]. For the time integrator, we use a PYTORCH implementation of differentiable ODE solvers, Torch Diff Eq [3]. All experiments are performed on MACBOOK PRO with 2.9 GHz i9 CPU and 32 GB memory. |
| Researcher Affiliation | Collaboration | Kookjin Lee School of Computing and Augmented Intelligence Arizona State University Tempe, AZ 85281 Nathaniel Trask Center for Computing Research Sandia National Laboratories Albuquerque, NM 87123 EMAIL Panos Stinis Pacific Northwest National Laboratory Richland, WA 99354 |
| Pseudocode | Yes | Algorithm 1: Neural ODE training; Algorithm 2: Penalty or GENERIC training |
| Open Source Code | No | The information is insufficient. The paper does not provide concrete access to source code for the methodology described, nor does it contain an explicit statement of code release or a repository link. |
| Open Datasets | Yes | Data for all considered benchmark problems can be found in [51]. |
| Dataset Splits | Yes | We then split the sequence into three segments, [0, ttrain], (ttrain, tval], and (tval, ttest] for training, validation, and test such that 0 < ttrain < tval < ttest. |
| Hardware Specification | Yes | All experiments are performed on MACBOOK PRO with 2.9 GHz i9 CPU and 32 GB memory. |
| Software Dependencies | Yes | We implement the algorithms in PYTHON 3.6.5, NUMPY 1.16.2, and PYTORCH 1.7.1 [48]. |
| Experiment Setup | Yes | For ODESolve, we use the Dormand Prince method (dopri5) [49] with relative tolerance 10 5 and absolute tolerance 10 6. The loss function L measures the discrepancy between the ground truth states and approximate states via mean absolute errors, and the network weights and biases are updated using Adamax [50] with an initial learning rate 0.01. ... For black-box neural ODEs, we simply use a stochastic gradient descent (SGD) optimizer to update the network weights and biases using the mini-batches on the observable states, {xo ℓ, xo ℓ+1, . . . , xo ℓ+L 1}. |