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

TL;DR

This paper introduces a meta-learning framework with physics-informed fine-tuning to develop a generalizable foundation model for chemical reactor modeling, capable of rapid adaptation across diverse reactor types with minimal data.

Contribution

The work presents a novel combination of meta-learning and physics-informed adaptation to create a flexible, efficient model for various chemical reactors, advancing foundation models in chemical engineering.

Findings

Superior few-shot adaptation across reactor types

Effective physics-informed fine-tuning for physical consistency

Outperforms conventional data-driven and transfer learning methods

Abstract

Developing accurate models for chemical reactors is often challenging due to the complexity of reaction kinetics and process dynamics. Traditional approaches require retraining models for each new system, limiting generalizability and efficiency. In this work, we take a step toward foundation models for chemical reactor modeling by introducing a neural network framework that generalizes across diverse reactor types and rapidly adapts to new chemical processes. Our approach leverages meta-learning to pretrain the model on a broad set of reactor dynamics, enabling efficient adaptation to unseen reactions with minimal data. To further enhance generalizability, we incorporate physics-informed fine-tuning, ensuring physically consistent adaptation to new reactor conditions. Our framework is evaluated across three integer-order fundamental reactor types - continuous stirred tank reactors, batch reactors, and plug flow reactors - demonstrating superior few-shot adaptation compared to conventional data-driven, physics-informed, and transfer learning approaches. By combining meta-learning with physics-informed adaptation, this work lays the foundation for a generalizable modeling framework, advancing the development of foundation models for chemical engineering applications. Source code is available at https://github.com/killingbear999/chemical-reactor-foundation-model.