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

• URL: http://arxiv.org/abs/2404.03660v1
• Date: Wed, 24 Jan 2024 04:41:03 GMT
• Title: Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework
• Authors: Xia Chen, Alexander Rex, Janis Woelke, Christoph Eckert, Boris Bensmann, Richard Hanke-Rauschenbach, Philipp Geyer,
• Abstract summary: We propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development. 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. 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.
• Score: 36.74423086415903
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training [92.88889953768455]
  Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge. We identify computational subgraphs that facilitate knowledge storage and processing.
  arXiv  Detail & Related papers  (2025-02-16T16:55:43Z)
• A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods [4.686190098233778]
  The paper highlights the benefits of integrating generative AI with knowledge bases, including improved data contextualization, enhanced model accuracy, and better utilization of knowledge resources. The findings provide a detailed overview of the current state of research, identify key gaps, and offer actionable recommendations.
  arXiv  Detail & Related papers  (2025-01-19T23:25:21Z)
• Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models [0.0]
  We propose a novel approach based on knowledge graphs to provide timely access to structured information. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable.
  arXiv  Detail & Related papers  (2024-09-30T07:08:28Z)
• On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
  Zero-shot image recognition (ZSIR) aims to recognize and reason in unseen domains by learning generalized knowledge from limited data. This paper thoroughly investigates recent advances in element-wise ZSIR and provides a basis for its future development.
  arXiv  Detail & Related papers  (2024-08-09T05:49:21Z)
• A Survey on Symbolic Knowledge Distillation of Large Language Models [8.237773729114926]
  Survey focuses on the emerging and critical area of symbolic knowledge distillation in Large Language Models. Describes the core challenges, including maintaining the depth of knowledge in a comprehensible format. Examines the various approaches and techniques that have been developed in this field.
  arXiv  Detail & Related papers  (2024-07-12T12:18:19Z)
• Towards Automated Knowledge Integration From Human-Interpretable Representations [55.2480439325792]
  We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection. We empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.
  arXiv  Detail & Related papers  (2024-02-25T15:08:37Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design [0.0]
  Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design and manufacturing. Here we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials.
  arXiv  Detail & Related papers  (2023-10-30T20:31:50Z)
• Pathway toward prior knowledge-integrated machine learning in engineering [1.3091722164946331]
  This study emphasizes efforts to integrate multidisciplinary domain professions into machine acknowledgeable, data-driven processes. This approach balances holist and reductionist perspectives in the engineering domain.
  arXiv  Detail & Related papers  (2023-07-10T13:06:55Z)
• Meta-learning using privileged information for dynamics [66.32254395574994]
  We extend the Neural ODE Process model to use additional information within the Learning Using Privileged Information setting. We validate our extension with experiments showing improved accuracy and calibration on simulated dynamics tasks.
  arXiv  Detail & Related papers  (2021-04-29T12:18:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.