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

In this work, we present a novel application of foundation models for chemical reactor modeling. Accurate modeling of real-world chemical reactors through first-principles is often challenging, and the process of rebuilding and retraining models for each new chemical process is inefficient. This raises a critical question: can we develop a single, universal neural network (i.e., a foundation model) that can rapidly adapt to any new chemical process in a reactor? To address this, we propose a foundation model for chemical reactor modeling that employs a meta-learning approach, followed by physics-informed fine-tuning on new tasks with only a few data samples. Our model is designed to generalize across three classic reactor types: continuous stirred tank reactors, batch reactors, and plug flow reactors. Compared to conventional methods such as data-driven learning, physics-informed learning, transfer learning, and meta-learning, our approach demonstrates superior performance in few-shot scenarios. Specifically, it shows rapid adaptation to unseen reactions with varying integer orders across different reactor set-ups, requiring minimal data for fine-tuning. Source code is available at https://github.com/killingbear999/chemical-reactor-foundation-model.