Contour location via entropy reduction leveraging multiple information sources
Authors: Alexandre Marques, Remi Lam, Karen Willcox
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present three examples that demonstrate the performance of CLo VER. The first two examples involve multiple information sources, and illustrate the reduction in computational cost that can be achieved by combining information from multiple sources in a principled way. The last example compares the performance of CLo VER to that of competing GP-based algorithms, showing that CLo VER can outperform existing alternatives even in the case of a single information source. |
| Researcher Affiliation | Academia | Alexandre N. Marques Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, MA 02139 noll@mit.edu Remi R. Lam Center for Computational Engineering Massachusetts Institute of Technology Cambridge, MA 02139 rlam@mit.edu Karen E. Willcox Institute for Computational Engineering and Sciences University of Texas at Austin Austin, TX 78712 kwillcox@ices.utexas.edu |
| Pseudocode | No | The paper describes the algorithm steps in numbered prose but does not provide structured pseudocode or an explicitly labeled algorithm block. |
| Open Source Code | Yes | An implementation of CLo VER in Python 2.7 is available at https://github.com/anmarques/CLo VER. |
| Open Datasets | No | The paper describes using mathematical functions (e.g., Branin-Hoo function) and dynamical models, which are not typically 'datasets' with concrete public access information. It does not provide links or citations to publicly available datasets used for the experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or detailed methodology) for training, validation, or testing their models. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud resources) used for running the experiments. |
| Software Dependencies | Yes | An implementation of CLo VER in Python 2.7 is available at https://github.com/anmarques/CLo VER. |
| Experiment Setup | Yes | In this example we use covariance kernels of the Matérn class [22] with ν = 5/2, and zero mean functions. [...] In the present example we adopt d0 = 0.002 and d1 = 0.0005 as the mean values for the length scales of Σ0 and Σ1, respectively. [...] The integration over D is performed with the trapezoidal rule on a 50 50 uniform grid, and the optimization set A is composed of a 30 30 uniform grid. |