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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos
Authors: Christian Pedersen, Laure Zanna, Joan Bruna
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate this approach on two high-dimensional turbulent systems, a forced 2D Navier-Stokes flow, and a 2-layer quasigeostrophic turbulent flow, enabling stable predictions over 1e5 emulator steps. |
| Researcher Affiliation | Academia | 1Courant Institute of Mathematical Sciences, New York University, USA 2Center for Data Science, New York University, USA. |
| Pseudocode | Yes | Algorithm 1 Algorithm for thermalized trajectories |
| Open Source Code | Yes | All model architecture, training and inference codes can be found at https://github.com/Chris-Pedersen/thermalizer. |
| Open Datasets | No | To build a training and test set, we use numerical simulations to generate a total of N = 500,000 trajectories. We use 450,000 of these for training and the remaining 50,000 for validation and testing. |
| Dataset Splits | Yes | We use 450,000 of these for training and the remaining 50,000 for validation and testing. |
| Hardware Specification | No | leveraging the speed of graphical processing units (GPUs) to provide fast approximate solutions. |
| Software Dependencies | No | We numerically solve the equations using the pseudospectral method with periodic boundary conditions from the publicly available code jax-cfd (Dresdner et al., 2022). This was implemented in PyTorch, the code for which will be made publicly available upon de-anonymization. |
| Experiment Setup | Yes | The network weights are optimized using the Adam W optimizer with momenta β1 = 0.9 and β2 = 0.999, and a learning rate of 5e-4. In practice, equation 6 is broken up into mini-batches of size 32. We train the model for 12 epochs... For the thermalizer... To optimise the network, equation 8 is broken up into mini-batches of size 64. Again we use the Adam W optimizer, with a learning rate of 2e-5, and train the thermalizer for 35 epochs. |