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Developing accurate models for chemical reactors is often challenging due tothe complexity of reaction kinetics and process dynamics. Traditionalapproaches require retraining models for each new system, limitinggeneralizability and efficiency. In this work, we take a step toward foundationmodels for chemical reactor modeling by introducing a neural network frameworkthat generalizes across diverse reactor types and rapidly adapts to newchemical processes. Our approach leverages meta-learning to pretrain the modelon a broad set of reactor dynamics, enabling efficient adaptation to unseenreactions with minimal data. To further enhance generalizability, weincorporate physics-informed fine-tuning, ensuring physically consistentadaptation to new reactor conditions. Our framework is evaluated across threeinteger-order fundamental reactor types - continuous stirred tank reactors,batch reactors, and plug flow reactors - demonstrating superior few-shotadaptation compared to conventional data-driven, physics-informed, and transferlearning approaches. By combining meta-learning with physics-informedadaptation, this work lays the foundation for a generalizable modelingframework, advancing the development of foundation models for chemicalengineering applications. Source code is available athttps://github.com/killingbear999/chemical-reactor-foundation-model.

Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation