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..
A Neural PDE Solver with Temporal Stencil Modeling
Authors: Zhiqing Sun, Yiming Yang, Shinjae Yoo
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that TSM achieves the new state-of-the-art simulation accuracy for 2-D incompressible Navier-Stokes turbulent flows |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, Pittsburgh, PA 15213, USA 2Brookhaven National Laboratory, Upton, NY 11973, USA. |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Our code is available at https: //github.com/Edward-Sun/TSM-PDE. |
| Open Datasets | No | Simulated data Following previous work (Kochkov et al., 2021), we train our method with 2-D Kolmogorov flow, a variant of incompressible Navier-Stokes flow with constant forcing f = sin(4y)ˆx 0.1u. All training and evaluation data are generated with a JAX-based2 finite volume-based direct numerical simulator in a staggered-square mesh (Mc Donough, 2007) as briefly described in Sec. 3.1. We refer the readers to the appendix of (Kochkov et al., 2021) for more data generation details. |
| Dataset Splits | No | We use 16 trajectories for evaluation. |
| Hardware Specification | Yes | We train and evaluate all the classic and neural Navier-Stokes solvers in 8 Nvidia Tesla V100-32G GPUs. The inference latency is measured by unrolling 2 trajectories for 25.0 simulation time on a single V100 GPU. |
| Software Dependencies | No | All training and evaluation data are generated with a JAX-based2 finite volume-based direct numerical simulator |
| Experiment Setup | Yes | We train the neural models on Re = 1000 flow data with density ρ = 1 and viscosity ν = 0.001 on a 2π x 2π domain, which results in a time-step of Δt = 7.0125 x 10−3 according to the Courant Friedrichs Lewy (CFD) condition on the 64x64 simulation grid. |