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Physics informed token transformer for solving partial differential equations
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DOI：10.1088/2632-2153/ad27e3.png)
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摘要
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Solving partial differential equations (PDEs) is the core of many fields of science and engineering. While classical approaches are often prohibitively slow, machine learning models often fail to incorporate complete system information. Over the past few years, transformers have had a significant impact on the field of Artificial Intelligence and have seen increased usage in PDE applications. However, despite their success, transformers currently lack integration with physics and reasoning. This study aims to address this issue by introducing Physics Informed Token Transformer (PITT). The purpose of PITT is to incorporate the knowledge of physics by embedding PDEs into the learning process. PITT uses an equation tokenization method to learn an analytically-driven numerical update operator. By tokenizing PDEs and embedding partial derivatives, the transformer models become aware of the underlying knowledge behind physical processes. To demonstrate this, PITT is tested on challenging 1D and 2D PDE operator learning tasks. The results show that PITT outperforms popular neural operator models and has the ability to extract physically relevant information from governing equations.
Keyword:
machine learning
neural operators
physics informed
期刊
M
IF:
4.6
论文数:
1.1K
被引数:
3.4K
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引用论文
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架
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