Hypernetwork-based meta-learning for low-rank physics-informed neural networks

W Cho, M Jo, H Lim, K Lee, D Lee, S Hong… - arXiv preprint arXiv …, 2024 - arxiv.org

Complex physical systems are often described by partial differential equations (PDEs) that
depend on parameters such as the Reynolds number in fluid mechanics. In applications …

被引用次数：14 相关文章

[PDF] arxiv.org

CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations

J Berman, B Peherstorfer - arXiv preprint arXiv:2402.14646, 2024 - arxiv.org

This work introduces reduced models based on Continuous Low Rank Adaptation
(CoLoRA) that pre-train neural networks for a given partial differential equation and then …

被引用次数：7 相关文章所有 3 个版本

[PDF] arxiv.org

Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation

Y Wang, J Bai, MS Eshaghi, C Anitescu… - arXiv preprint arXiv …, 2025 - arxiv.org

AI for PDEs has garnered significant attention, particularly Physics-Informed Neural
Networks (PINNs). However, PINNs are typically limited to solving specific problems, and …

Physics-Informed Neuro-Evolution (PINE): A Survey and Prospects

JC Wong, A Gupta, CC Ooi, PH Chiu, J Liu… - arXiv preprint arXiv …, 2025 - arxiv.org

Deep learning models trained on finite data lack a complete understanding of the physical
world. On the other hand, physics-informed neural networks (PINNs) are infused with such …

[PDF] arxiv.org

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