Style Editor Docs
     
  • Home
  • Products & Services
    • DataHowLab
    • Professional Services
    • Innovation Hub
  • Resources
    • Technology
    • Publications
    • Bioprocess Blog
  • Education & Events
    • Schedule events & trainings
    • Trainings
    • Upcoming Events
  • About
    • DataHow
    • Team
    • Careers
    • Contact
   info@datahow.ch
  • Home
  • Products & Services
    • DataHowLab
    • Professional Services
    • Innovation Hub
  • Resources
    • Technology
    • Publications
    • Bioprocess Blog
  • Education & Events
    • Schedule events & trainings
    • Trainings
    • Upcoming Events
  • About
    • DataHow
    • Team
    • Careers
    • Contact
    Publications  ·  15. February 2022

    Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions

    Full title: 

    Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions

     

    Abstract:

    Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledgeor black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the synergy between the two paradigms has emerged as a pragmatic compromise. The data-driven part of these has been largely based on conventional machine learning algorithms (e.g., artificial neural network, support vector regression), which prevents interpretability of the finally learnt model by the domain experts. In this work, we present a novel hybrid modeling framework, the Functional-Hybrid model, that uses the ranked domain-specific functional beliefs together with symbolic regression to develop dynamic models. We demonstrate the successful implementation of the Functional-Hybrid model and its interpretability, focusing on applying chemical reaction kinetic principles to classical chemical reactions, biochemistry, ecology, physiology, and a bioreactor. Furthermore, we demonstrate that during interpolation, the Functional-Hybrid model performs similarly to a Hybrid-ANN hybrid model implementing a conventional ANN. However, it provides the advantage of being –to some extent– interpretable, unlike the conventional Hybrid-ANN model. Additionally, it is shown that the Functional-Hybrid model outperforms the Hybrid-ANN model for a very low number of experiments, making it more suitable when data is scarce. Finally, the Functional-Hybrid models show superior extrapolation capabilities compared to the Hybrid-ANN model. This improved performance can be attributed to the structure imposed by the functional transformations introduced in the Functional-Hybrid model.

    Full Publication
    tagPlaceholderTags:

    Write a comment

    Comments: 0
    draggable-logo

    Hagenholzstrasse.111 / 8050 Zurich/ info@datahow.ch 


    Schedule a Demo

    Edit here your navigation button
    is-switcher admin-only
    is-switcher admin-only
    is-switcher admin-only

     

    Main colors
       bg-primary
       bg-primary-light
       bg-primary-dark
       bg-secondary
       bg-secondary-dark
    Template sections
       body
       top-header
       header
       content
    Footer Styles
       background
       text color
       link color
       horizontal line
    Buttons
       style 1
       style 2
       style 3
    Other elements
      social icons
      navigation color
      subnav background
    Mobile navigation
       background color
       navigation color
    Template configurations
    g-font
    navigation styles
    size-17 weight-400
    content styles
    form-white
    footer styles
    o-form color-white
    Typography
    Heading H1
    weight-600
    Heading H2
    weight-600
    Heading H3
    weight-600
    Buttons
    weight-600 is-uppercase
    Animations
    wow animated fadeInUp

    Note:
    All changes made here will be applied to your entire website.

    About | Privacy Policy | Cookie Policy | Sitemap
    © Copyright 2022 - DataHow - All rights reserved
    Log out | Edit
    • Scroll to top