迈向化学反应器建模的基础模型：结合物理信息自适应的元学习
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
AI总结：
本研究提出结合元学习与物理信息微调的神经网络框架，实现化学反应器模型跨类型泛化与少样本高效适应，为化工基础模型奠定基础。
摘要：
由于反应动力学和过程动态的复杂性，为化学反应器建立精确模型通常具有挑战性。传统方法需要为每个新系统重新训练模型，限制了泛化能力和效率。在本研究中，我们向化学反应器建模的基础模型迈出了一步，引入了一个能够在多种反应器类型间泛化并快速适应新化学过程的神经网络框架。我们的方法利用元学习在广泛的反应器动态数据集上预训练模型，使其能够以最少的数据高效适应未见反应。为进一步增强泛化能力，我们引入了物理信息微调，确保对新反应器条件的适应在物理上保持一致。我们的框架在三种整数级基本反应器类型——连续搅拌釜反应器、间歇反应器和活塞流反应器上进行了评估，与传统的数据驱动、物理信息和迁移学习方法相比，展现出卓越的少样本适应能力。通过将元学习与物理信息自适应相结合，本研究为可泛化建模框架奠定了基础，推动了化学工程应用中基础模型的发展。源代码可在 https://github.com/killingbear999/chemical-reactor-foundation-model 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
