We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| tum-pbs/unrolling | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| I | Ran | tum-pbs/unrolling/kuramoto-sivashinsky/ks_train.py code served (permissive licence) · get_code("15357abf73006dd3") |
| result_type | Ran | tum-pbs/unrolling/kuramoto-sivashinsky/ks_eval.py code served (permissive licence) · get_code("ad50f18b5044293c") |
| rolling_mean | Ran | tum-pbs/unrolling/kuramoto-sivashinsky/ks_train.py code served (permissive licence) · get_code("33aa807cc0f4b431") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Unrolling training trajectories over time strongly influences the inference accuracy of neural network-augmented physics simulators. We analyze this in three variants of training neural time-steppers. In addition to one-step setups and fully differentiable unrolling, we include a third, less widely used variant: unrolling without temporal gradients. Comparing networks trained with these three modalities disentangles the two dominant effects of unrolling, training distribution shift and long-term gradients. We present detailed study across physical systems, network sizes and architectures, training setups, and test scenarios. It also encompasses two simulation modes: In prediction setups, we rely solely on neural networks to compute a trajectory. In contrast, correction setups include a numerical solver that is supported by a neural network. Spanning these variations, our study provides the empirical basis for our main findings: Non-differentiable but unrolled training with a numerical solver in a correction setup can yield substantial improvements over a fully differentiable prediction setup not utilizing this solver. The accuracy of models trained in a fully differentiable setup differs compared to their non-differentiable counterparts. Differentiable ones perform best in a comparison among correction networks as well as among prediction setups. For both, the accuracy of non-differentiable unrolling comes close. Furthermore, we show that these behaviors are invariant to the physical system, the network architecture and size, and the numerical scheme. These results motivate integrating non-differentiable numerical simulators into training setups even if full differentiability is unavailable. We show the convergence rate of common architectures to be low compared to numerical algorithms. This motivates correction setups combining neural and numerical parts which utilize benefits of both.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.12971")
get_code_for_paper("2402.12971")
have("2402.12971")
Connect an agent — have() is free.