用生成流网络让数字孪生自动校准，从模糊观测中找出合理参数。
Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems
- 将校准问题转化为生成模型，按模拟与观测吻合度采样参数
- 在番茄生长模型上找回主要可行参数区域，且保留多种可能解
- 适合需处理不确定性的自然系统建模场景
自然系统数字孪生需随物理系统演化持续校准，但观测稀疏且间接，机制模型参数难以直接测量。此时模型校准本质上是基于仿真的推断问题，但有限观测常无法唯一确定最优参数，导致多个参数配置均与证据兼容。本文提出一种基于生成流网络（GFlowNet）的数字孪生模型适应方法，将适应过程建模为完整仿真配置空间上的生成问题，使得可采样出与观测行为匹配度高的参数化方案。通过基于机制番茄模型的受控环境农业案例研究，验证了所学策略能有效恢复适应景观中的主要区域，发现强校准假设，并在不确定性下保持多个合理配置。
原文摘要 · Abstract (English)
Digital twins of natural systems must remain aligned with physical systems that evolve over time, are only partially observed, and are typically modeled by mechanistic simulators whose parameters cannot be measured directly. In such settings, model adaptation is naturally posed as a simulation-based inference problem. However, sparse and indirect observations often fail to identify a unique and optimal calibration, leaving several simulator parameterizations compatible with the available evidence. This article presents a GFlowNet-based approach to model adaptation for digital twins of natural systems. We formulate adaptation as a generative modeling problem over complete simulator configurations, so that plausible parameterizations can be sampled with probability proportional to a reward derived from agreement between simulated and observed behavior. Using a controlled environment agriculture case study based on a mechanistic tomato model, we show that the learned policy recovers dominant regions of the adaptation landscape, retrieves strong calibration hypotheses, and preserves multiple plausible configurations under uncertainty.
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