ChemML: Physics-Informed AI/ML of Geochemical Datasets for Characterization, Parameterization, and Prediction of Contaminant Transport and Remediation Processes

Summary

This work describes ChemML as a physics-informed AI/ML framework for analyzing geochemical datasets and improving the characterization, parameterization, and prediction of contaminant transport and remediation processes. The approach emphasizes robust, trustworthy, and defensible science-informed models that can overcome limitations of traditional geochemical simulations while supporting cloud-based environmental decision and remediation workflows.

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