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Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs
Zhangyong Li · 2026-05-14 · via cs.LG updates on arXiv.org

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Abstract:Physics-Informed Neural Networks (PINNs) for high-dimensional and high-order partial differential equations (PDEs) are primarily constrained by the $\mathcal{O}(d^k)$ spatial derivative complexity and the $\mathcal{O}(P)$ memory overhead of backpropagation (BP). While randomized spatial estimators successfully reduce the spatial complexity to $\mathcal{O}(1)$, their reliance on first-order optimization still leads to prohibitive memory consumption at scale. Zeroth-order (ZO) optimization offers a BP-free alternative; however, naively combining randomized spatial operators with ZO perturbations triggers a variance explosion of $\mathcal{O}(1/\varepsilon^2)$, leading to numerical divergence. To address these challenges, we propose the \textbf{S}tochastic \textbf{D}imension-free \textbf{Z}eroth-order \textbf{E}stimator (\textbf{SDZE}), a unified framework that achieves dimension-independent complexity in both space and memory. Specifically, SDZE leverages \emph{Common Random Numbers Synchronization (CRNS)} to algebraically cancel the $\mathcal{O}(1/\varepsilon^2)$ variance by locking spatial random seeds across perturbations. Furthermore, an \emph{implicit matrix-free subspace projection} is introduced to reduce parameter exploration variance from $\mathcal{O}(P)$ to $\mathcal{O}(r)$ while maintaining an $\mathcal{O}(1)$ optimizer memory footprint. Empirical results demonstrate that SDZE enables the training of 10-million-dimensional PINNs on a single NVIDIA A100 GPU, delivering significant improvements in speed and memory efficiency over state-of-the-art baselines.
Comments: arXiv admin note: text overlap with arXiv:2412.00088, arXiv:2410.08989, arXiv:2307.12306 by other authors
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2603.24002 [cs.LG]
  (or arXiv:2603.24002v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2603.24002

arXiv-issued DOI via DataCite

Submission history

From: Zhangyong Liang [view email]
[v1] Wed, 25 Mar 2026 07:02:34 UTC (99 KB)
[v2] Wed, 13 May 2026 12:52:06 UTC (98 KB)

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