Neural Conjugate Flows: A Physics-Informed Architecture with Flow Structure
Authors: Arthur Bizzi, Lucas Nissenbaum, João M. Pereira
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate in numerical experiments how this topological group structure leads to concrete computational gains over other physics informed neural networks in estimating and extrapolating latent dynamics of ODEs, while training up to five times faster than other flow-based architectures. We present numerical experiments. |
| Researcher Affiliation | Academia | Instituto de Matem atica Pura e Aplicada (IMPA), Rio de Janeiro, Brazil EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes procedural steps and architectures using figures and text (e.g., Figure 4: The NCF pipeline, section 3.1 Neural Conjugation), but it does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/arthur-bizzi/Neural-Conjugate Flows-AAAI |
| Open Datasets | No | The paper uses synthetic data generated from numerical integration of models (Fitz Hugh-Nagumo, Hodgkin-Huxley) and does not provide specific access information (link, DOI, repository, or formal citation for a dataset) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes subdividing time intervals for sampling training points (e.g., "subdivided the time-interval uniformly in N = 100 samples ti") but does not specify distinct training, validation, and test dataset splits in terms of percentages, absolute counts, or predefined partitions for reproducibility. |
| Hardware Specification | Yes | They were executed on the same machine, equipped with an AMD Ryzen 9 5900HX processor, an RTX 3060 GPU and 16GB of RAM. |
| Software Dependencies | No | The paper mentions using "Pytorch" and the "Torch Dyn library" but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | Each model was trained for 2000 epochs, full-batch, and optimized with ADAM (Kingma and Ba 2014). The optimizer was set up with Learning rate α = 1 10 3 and decay parameters β = (0.9, 0.99) for the first experiment, and α = 2.5 10 3 and β = (0.9, 0.95) for the second. It also mentions Xavier initialization and tanh activations, and a Gaussian Fourier feature layer with σ = 2. |