Physics-informed Diffusion Generative Model for Time-Series Data Synthesis in Dynamic Systems
Industrial time-series signals, such as turbine temperature and rotational speed in aero-engines, are essential for monitoring the health and operational status of complex dynamical systems. However, collecting such data is often limited by harsh environments (e.g., high temperature and high pressure) and the high cost of experimental testing. To address this challenge, we introduce PhysDGM, a stepwise physics-embedded diffusion generative model for synthesizing time-series data that are consistent with the underlying physical laws of dynamical systems. PhysDGM embeds physical laws directly into each reverse diffusion step of the generative process, ensuring trajectory-level physical cons...
| Authors | Haiteng Wang, Yunfei Zhu, Tao Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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