# A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes

arXiv: https://arxiv.org/abs/2112.01475 · HTML: https://mlsanity.com/paper/2112.01475/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 126 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows](https://mlsanity.com/paper/1911.11380/) (2019)

## Built on by (most cited first, 7 of 7)

- [Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data](https://mlsanity.com/paper/2406.01528/) (2024)
- [Physics-Constrained Machine Learning for Chemical Engineering](https://mlsanity.com/paper/2508.20649/) (2025)
- [Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems](https://mlsanity.com/paper/2410.10023/) (2024)
- [Decentralized digital twins of complex dynamical systems](https://mlsanity.com/paper/2207.12245/) (2022)
- [Quantum Synthetic Data Generation for Industrial Bioprocess Monitoring](https://mlsanity.com/paper/2510.17688/) (2025)
- [Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data](https://mlsanity.com/paper/2609.04011/) (2026)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

This study presents a broad perspective of hybrid process modeling and optimization combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories. The first refers to the case where a data-based ML model compliments and makes the first-principle science-based model more accurate in prediction, and the second corresponds to the case where scientific knowledge helps make the ML model more scientifically consistent. We present a detailed review of scientific and engineering literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of the sub-categories of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the sub-categories of science-guided design, learning and refinement. For each sub-category, we identify its requirements, advantages and limitations, together with their published and potential areas of applications in bioprocessing and chemical engineering.We also present several examples to illustrate different hybrid SGML methodologies for modeling polymer processes.