Abstract:Physics-Informed Neural Networks have become a powerful mesh-free method for solving partial differential equations, but their performance is often limited by spectral bias. Specifically, in standard MLPs used in PINNs, the global parameter coupling causes the model to prioritize learning low-frequency components, resulting in slow convergence for high-frequency details. To overcome this limitation, we introduce the Physics-Informed Local Implicit Representation (PILIR). Our approach separates the global physical domain into a discrete latent feature space and a continuous generative decoder. By using a learnable grid to encode explicit spatial locality, PILIR can capture high-frequency details locally, preventing dilution by global patterns. A generative neural operator then synthesizes these local latent features into continuous physical fields, allowing accurate reconstruction of fine-scale structures. Experiments on a range of challenging PDEs show that PILIR effectively mitigates spectral bias, thereby boosting the convergence of high-frequency details and achieving superior accuracy compared to state-of-the-art methods.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.00385 [cs.LG] |
| (or arXiv:2605.00385v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00385 arXiv-issued DOI via DataCite (pending registration) |
From: Jianfeng Li [view email]
[v1]
Fri, 1 May 2026 04:07:39 UTC (10,089 KB)
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