Learning to Accelerate Partial Differential Equations via Latent Global Evolution
Authors: Tailin Wu, Takashi Maruyama, Jure Leskovec
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method in a 1D benchmark of nonlinear PDEs, 2D Navier-Stokes flows into turbulent phase and an inverse optimization of boundary conditions in 2D Navier-Stokes flow. |
| Researcher Affiliation | Collaboration | Tailin Wu Department of Computer Science Stanford University tailin@cs.stanford.edu Takashi Maruyama NEC Corp. & Stanford University 49takashi@nec.com & takashi279@cs.stanford.edu Jure Leskovec Department of Computer Science Stanford University jure@cs.stanford.edu |
| Pseudocode | No | The paper does not contain a pseudocode block or an algorithm block. |
| Open Source Code | Yes | 1Project website and code can be found at http://snap.stanford.edu/le_pde/. |
| Open Datasets | Yes | We use the 1D benchmark in [7], whose PDEs are... We test LE-PDE in a 2D benchmark [14] of Navier-Stokes equation. |
| Dataset Splits | No | We perform hyperparameter search over latent dimension of {64, 128} and use the model with best validation performance. While this implies a validation set was used, no specific details about the data split (e.g., percentages or counts) are provided in the main text. |
| Hardware Specification | No | The paper states "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] In Appendix D,E,F,G." This indicates hardware details are in the appendix, but they are not explicitly described in the main text itself. |
| Software Dependencies | No | The paper mentions "PhiFlow [73] as our ground-truth solver", but does not specify a version number for this or any other key software dependencies. |
| Experiment Setup | Yes | We perform hyperparameter search over latent dimension of {64, 128} and use the model with best validation performance. To ensure a fair comparison, here our LE-PDE uses past 10 steps to predict one future step and temporal bundling S = 1 (no bundling), the same setting as in FNO-2D. |