Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos
Authors: Christian Pedersen, Laure Zanna, Joan Bruna
ICML 2025 | Venue PDF | Archive PDF | Plain Text | 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. |