tum-pbs/apebench
[Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (≥46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled Training; Rollout Metrics)
This tool helps researchers and engineers who work with complex physical simulations evaluate how well deep learning models can predict the behavior of systems governed by Partial Differential Equations (PDEs). You input various PDE scenarios and neural network architectures, and it outputs performance metrics and visualizations of the neural network's predictions over time, comparing them to a high-fidelity simulator. This is for computational scientists, machine learning researchers, or simulation engineers who develop or assess data-driven models for physical systems.
Use this if you need to systematically benchmark and compare different autoregressive neural network models for emulating a wide range of PDE-driven physical phenomena.
Not ideal if you are looking for a general-purpose PDE solver or a tool for real-time deployment of pre-trained models.
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Language
Python
License
MIT
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Last pushed
Nov 11, 2025
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