mlsanityData to 7 Oct 2026

Maximizing information from chemical engineering data sets: Applications to machine learning

arXiv 2201.10035 ↗First posted 25 Jan 2022stat.ML cs.AI cs.LGcited by 17

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Abstract (arXiv)

It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for many chemical engineering applications. This review discusses how challenging data characteristics arise in chemical engineering applications. We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult: (1) high variance, low volume data, (2) low variance, high volume data, (3) noisy/corrupt/missing data, and (4) restricted data with physics-based limitations. For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges. Finally, we identify several challenges for future research.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 35 of its 213 references that are in the map. First posted on arXiv 25 Jan 2022.

Builds on

35 of 213 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 1; 2024: 3; 2025: 6; 2026: 3.

17 papers.

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  3. 2022SnAKe: Bayesian Optimization with Pathwise Explorationcited by 14
  4. 2022Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spacescited by 14
  5. 2024Transition Constrained Bayesian Optimization via Markov Decision Processescited by 7
  6. 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
  7. 2024System-Aware Neural ODE Processes for Few-Shot Bayesian Optimizationcited by 3
  8. 2025Training Neural ODEs Using Fully Discretized Simultaneous Optimizationcited by 3
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  10. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  11. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineeringcited by 2
  12. 2023Physics Informed Piecewise Linear Neural Networks for Process Optimizationcited by 1
5 more
  1. 2024A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plantcited by 0
  2. 2025Efficient Training of Physics-enhanced Neural ODEs via Direct Collocation and Nonlinear Programmingcited by 0
  3. 20253W Dataset 2.0.0: a realistic and public dataset with rare undesirable real events in oil wellscited by 0
  4. 2026Meta-learning for sample-efficient Bayesian optimisation of fed-batch processescited by 0
  5. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0