![[2602.19475] Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.19475] Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
Summary
The paper presents Scale-PINN, a novel approach to enhance Physics-Informed Neural Networks (PINNs) by integrating a Sequential Correction Algorithm, significantly improving training speed and accuracy for solving partial differential equations.
Why It Matters
This research is crucial as it addresses the limitations of traditional PINNs in computational efficiency and accuracy, making them more applicable in scientific and engineering fields. By bridging deep learning with numerical methods, Scale-PINN could lead to faster and more reliable solutions for complex physical problems.
Key Takeaways
- Scale-PINN integrates numerical algorithms into PINN training, enhancing efficiency.
- The method reduces training time for complex problems from hours to under 2 minutes.
- It maintains high accuracy, making it suitable for various applications in physics.
- The approach represents a significant shift in how PINN losses are constructed.
- Scale-PINN could facilitate broader adoption of PINNs in scientific research.
Computer Science > Computational Engineering, Finance, and Science arXiv:2602.19475 (cs) [Submitted on 23 Feb 2026] Title:Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction Authors:Pao-Hsiung Chiu, Jian Cheng Wong, Chin Chun Ooi, Chang Wei, Yuchen Fan, Yew-Soon Ong View a PDF of the paper titled Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction, by Pao-Hsiung Chiu and 5 other authors View PDF HTML (experimental) Abstract:Physics-informed neural networks (PINNs) have emerged as a promising mesh-free paradigm for solving partial differential equations, yet adoption in science and engineering is limited by slow training and modest accuracy relative to modern numerical solvers. We introduce the Sequential Correction Algorithm for Learning Efficient PINN (Scale-PINN), a learning strategy that bridges modern physics-informed learning with numerical algorithms. Scale-PINN incorporates the iterative residual-correction principle, a cornerstone of numerical solvers, directly into the loss formulation, marking a paradigm shift in how PINN losses can be conceived and constructed. This integration enables Scale-PINN to achieve unprecedented convergence speed across PDE problems from different physics domain, including reducing training time on a challenging fluid-dynamics problem for state-of-the-art PINN from hours to sub-2 minutes while maintaining superior accuracy, and enabling application to re...