Terrestrial ecosystems constitute a major component of the global carbon sink and play a critical role in regulating the global carbon cycle. Although process-based models such as the Ecosystem Demography (ED) model are widely used to simulate these dynamics and widely adopted in research and applications, they remain computationally intensive and are not well suited for large-scale (e.g., global) projections at high spatial and temporal resolution, or under wide-range of future scenarios. AI-based emulators of process-based physical models have emerged as promising ways to accelerate the computation. However, there are several challenges in developing emulators for ecosystem processes, including error accumulation over long sequences, single-step initial conditions, and high-dimensional environmental conditions. Existing works often rely on time-series patterns in look-back windows, which are not well-suited for the problem with single-step initial conditions. Moreover, they often do not consider uncertainty, making it hard to know when the approximations are highly confident and when the results may need to be updated, e.g., by the process-based models. To address these limitations, we introduce EcoDiffusion, a conditional diffusion framework tailored for ecosystem dynamics emulation. We evaluated EcoDiffusion at locations distributed worldwide under different scenarios and showed that it demonstrated significant improvements over existing models.

A multivariable nonlinear predictive control algorithm based on online fuzzy modeling and discrete optimization is presented for a family of complex systems with strong nonlinearity. The algorithm consists of two part: The first part is online fuzzy modeling using fuzzy clustering and linear identification, the second part is discrete optimization of the control action based on the principle of Branch and Bound method. In the process of fuzzy modeling, the unsupervised fuzzy competitive algorithm and a discarding criterion are introduced to ensure the fuzzy model can trace the system dynamics in time. The effectiveness and advantage of the presented algorithm are illustrated two numerical examples.