发表机构
HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（瑞士西部瓦莱应用科学与艺术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究用JAX实现了可并行、可微的3-PG森林生长模型，通过加速计算提升效率，支持梯度优化与贝叶斯校准，结果与r3PG一致，拓展了3-PG模型的应用场景。
AI 中文摘要
基于过程的森林模型被广泛用于模拟森林生长及其对环境变化的响应，但其校准与应用通常需要大量计算成本高昂的模型评估。我们提出了用JAX实现的生理过程预测生长（Physiological Processes Predicting Growth，简称3-PG）模型，该模型利用即时编译、向量化和GPU加速来减少执行时间。此实现还支持自动微分，可提供模型输出及校准目标相对于模型参数的梯度，这使得高效的基于梯度的优化和梯度引导的贝叶斯校准成为可能，将3-PG模型的应用范围扩展到了传统无梯度方法之外。在评估的配置下，该实现产生的结果与r3PG数值一致。总体而言，这个JAX实现为3-PG模型的校准与应用提供了更快且可微的框架。
英文摘要
Process-based forest models are widely used to simulate forest growth and responses to environmental change, but their calibration and application often require many computationally expensive model evaluations. We present an implementation of the Physiological Processes Predicting Growth (3-PG) model in JAX that uses just-in-time compilation, vectorization, and GPU acceleration to reduce execution time. The implementation also supports automatic differentiation, providing gradients of model outputs and calibration objectives with respect to model parameters. This enables efficient gradient-based optimization and gradient-informed Bayesian calibration, extending 3-PG beyond conventional gradient-free approaches. The implementation produced results numerically consistent with r3PG for the evaluated configuration. Overall, the JAX implementation provides a faster and differentiable framework for calibrating and applying the 3-PG model.
