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arXiv · 2502.07636

Consistency Training with Physical Constraints

Che-Chia Chang·Chen-Yang Dai·Te-Sheng Lin·Ming-Chih Lai·Chieh-Hsin Lai
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Abstract

We propose a physics-aware Consistency Training (CT) method that accelerates sampling in Diffusion Models with physical constraints. Our approach leverages a two-stage strategy: (1) learning the noise-to-data mapping via CT, and (2) incorporating physics constraints as a regularizer. Experiments on toy examples show that our method generates samples in a single step while adhering to the imposed constraints. This approach has the potential to efficiently solve partial differential equations (PDEs) using deep generative modeling.

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Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin, Ming-Chih Lai, Chieh-Hsin Lai. 2025-02-11. Consistency Training with Physical Constraints. https://arxiv.org/abs/2502.07636

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