Toward a world model for corrosion science
Corrosion is a trajectory, not an event, yet current machine learning maps fixed inputs to outputs and cannot model reliably how a corroding interface responds to intervention. We argue that world models, deep learning frameworks that learn latent physical dynamics from observation sequences, can close this gap. A physically interpretable latent space anchored to corrosion mechanisms can enable prediction, counterfactual reasoning, and discovery. We chart a path toward realis
Current machine learning models struggle to predict how corrosion responds to interventions because they treat corrosion as a static event rather than a dynamic process. The authors propose utilizing 'world models,' a type of deep learning framework that learns underlying physical dynamics from observed sequences. By anchoring a physically interpretable latent space to specific corrosion mechanisms, these models could enable more accurate predictions, counterfactual reasoning, and the discovery of new insights. This paper outlines a potential pathway for developing such advanced models in corrosion science. The goal is to move beyond simple input-output mappings to a more nuanced understanding of material degradation. Such a system could revolutionize how we approach corrosion prevention and management.
This research proposes a new machine learning approach to better understand and predict material corrosion, moving beyond static models.
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