Towards foundation model for chemical reactor modeling: Meta-learning with physics-informed adaptation (2024-05-20T00:00:00.000000Z)

TL;DR

This work introduces a neural network framework that generalizes across diverse reactor types and rapidly adapts to new chemical processes, leveraging meta-learning to pretrain the model on a broad set of reactor dynamics, enabling efficient adaptation to unseen reactions with minimal data.

Authors

Zihao Wang

3 papers

Zhen Wu

3 papers

TL;DR

Authors

References61 items

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Financial support from the A*STAR MTC YIRG 2022 Grant (222K3024) and NRF-CRP Grant 27-2021-0001 is gratefully acknowledged.

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