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Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework

arXiv 2404.03660 ↗First posted 24 Jan 2024cs.LG cs.CEcited by 0

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Abstract (arXiv)

In this study, we propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development. Given the significance of PEMWE in green hydrogen production and the inherent challenges in optimizing its performance, our framework aims to meld data-driven models with domain-specific insights systematically to address the domain challenges. We first identify the uncertainties originating from data acquisition conditions, data-driven model mechanisms, and domain expertise, highlighting their complementary characteristics in carrying information from different perspectives. Building upon this foundation, we showcase how to adeptly decompose knowledge and extract unique information to contribute to the data augmentation, modeling process, and knowledge discovery. We demonstrate a hierarchical three-level framework, termed the "Ladder of Knowledge-integrated Machine Learning", in the PEMWE context, applying it to three case studies within a context of cell degradation analysis to affirm its efficacy in interpolation, extrapolation, and information representation. This research lays the groundwork for more knowledge-informed enhancements in ML applications in engineering.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 76 references that are in the map. First posted on arXiv 24 Jan 2024.

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References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2020Supervised Contrastive Learningcited by 2,829
  3. 2018End-to-End Incremental Learningcited by 597
  4. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 443
  5. 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 335
  6. 2019Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systemscited by 205
  7. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
  8. 2020Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemscited by 164
  9. 2017Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modelingcited by 131
  10. 2023Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical lawscited by 55
  11. 2021Asymmetric Loss Functions for Learning with Noisy Labelscited by 46
  12. 2020Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamicscited by 32
6 more
  1. 2022Masked Siamese ConvNetscited by 22
  2. 2021Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteriescited by 8
  3. 2022Introducing causal inference in the energy-efficient building design processcited by 7
  4. 2023Utilizing Domain Knowledge: Robust Machine Learning for Building Energy Prediction with Small, Inconsistent Datasetscited by 2
  5. 2023Pathway toward prior knowledge-integrated machine learning in engineeringcited by 2
  6. 2023Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industrycited by 2

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