Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
Authors: Naoya Takeishi, Alexandros Kalousis
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed experiments on two synthetic datasets and two real-world datasets, for which we prepared instances of physics-integrated VAEs. |
| Researcher Affiliation | Academia | University of Applied Sciences and Arts Western Switzerland (HES-SO) Geneva, Switzerland |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to its source code, nor does it explicitly state that the code for their methodology is available. |
| Open Datasets | Yes | We used images of galaxy of the Galaxy10 dataset [18]. |
| Dataset Splits | Yes | We generated 2,500 sequences of length τ = 50 with t = 0.05 and separated them into a training, validation, and test sets with 1,000, 500, and 1,000 sequences, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In the experiment with the advection-diffusion dataset reported in Table 1, the selected values of the hyperparameters were α = 0.1, β = 0.01, and γ = 10^6, which were chosen from only eight candidates (see Appendix E for detail). |