When Does Jacobian Fine-Tuning Stabilize Neural PDE Emulators?

Shreeyam Bangera; Manya Gupta

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arXiv · 2610.42013

When Does Jacobian Fine-Tuning Stabilize Neural PDE Emulators?

Shreeyam Bangera·Manya Gupta

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Abstract

Neural emulators of chaotic PDEs are trained on one-step prediction but deployed autoregressively, where their errors compound. Jacobian-based regularisers aim to curb this growth, but in a chaotic system part of the growth is physical and cannot be trained away. We separate the two sources of this error with a first-order decomposition. We compare the emulator's error with a counterfactual that propagates the same one-step mistakes through the solver's Jacobian, giving an amplification ratio: the factor by which the model's own dynamics amplify its errors. We study 21 configurations across four PDEs, five architectures and two model sizes (8-10k and 33k parameters), with errors measured at one Lyapunov time. Jacobian fine-tuning reduced the error by more than 10% for 1 of 34 seeds whose amplification ratio was at most 1.25, and for all 20 seeds whose ratio was at least four; for the latter, the error fell by a factor of 8 (small models) and 55 (33k models) in geometric mean over seeds. The gain approximately equals the one-step accuracy cost of fine-tuning divided by the amplification ratio. Matching the solver's Jacobian along the model's own error direction (EDJM) outperformed random directions at the same cost on 28 of 35 seeds; input-noise injection performed comparably to EDJM.

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Shreeyam Bangera, Manya Gupta. 2026-10-30. When Does Jacobian Fine-Tuning Stabilize Neural PDE Emulators?. https://arxiv.org/abs/2610.42013

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