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Geospatial observations combined with computational models have become key tounderstanding the physical systems of our environment and enable the design ofbest practices to reduce societal harm. Cloud-based deployments help to scaleup these modeling and AI workflows. Yet, for practitioners to make robustconclusions, model tuning and testing is crucial, a resource intensive processwhich involves the variation of model input variables. We have developed theVariational Exploration Module which facilitates the optimization andvalidation of modeling workflows deployed in the cloud by orchestratingworkflow executions and using Bayesian and machine learning-based methods toanalyze model behavior. User configurations allow the combination of diversesampling strategies in multi-agent environments. The flexibility and robustnessof the model-agnostic module is demonstrated using real-world applications.

Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows