Geospatial observations combined with computational models have become key to understanding the physical systems of our environment and enable the design of best practices to reduce societal harm. Cloud-based deployments help to scale up these modeling and AI workflows. Yet, for practitioners to make robust conclusions, model tuning and testing is crucial, a resource intensive process which involves the variation of model input variables. We have developed the Variational Exploration Module which facilitates the optimization and validation of modeling workflows deployed in the cloud by orchestrating workflow executions and using Bayesian and machine learning-based methods to analyze model behavior. User configurations allow the combination of diverse sampling strategies in multi-agent environments. The flexibility and robustness of the model-agnostic module is demonstrated using real-world applications.

翻译：地理空间观测与计算模型的结合已成为理解环境物理系统的关键，并推动最佳实践设计以减轻社会危害。基于云的部署有助于扩展这些建模和AI工作流。然而，为确保从业者得出可靠结论，模型调优与测试至关重要——这一资源密集型过程涉及模型输入变量的变化。我们开发了变异探索模块，通过协调工作流执行并采用贝叶斯及基于机器学习的方法分析模型行为，促进云端部署建模工作流的优化与验证。用户配置允许多智能体环境中多样化采样策略的组合。该模块的模型无关特性及其灵活性与鲁棒性已在真实应用中得到验证。