Target Parameterization in Diffusion Models for Nonlinear Spatiotemporal System Identification
Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed outputs. However, classical identification and forecasting approaches become markedly less reliable in turbulent-flow regimes, where the dynamics are high-dimensional, strongly nonlinear, and highly sensitive to compounding rollout errors. Diffusion-based models have recently shown improved robustness in this setting and offer probabilistic inference capabilities, but many current implementations inherit target parameterizations from image generation, most commonly noise or velocity prediction. In this work, we revisit this design choice in the co...
| Authors | Achraf El Messaoudi, Noureddine Khaous, Karim Cherifi |
|---|---|
| Primary category | eess.SY |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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