Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computational Engineering, Finance, and Science

arXiv:2407.17169 (cs)
[Submitted on 24 Jul 2024]

Title:KnowTD-An Actionable Knowledge Representation System for Thermodynamics

Authors:Luisa Vollmer, Sophie Fellenz, Fabian Jirasek, Heike Leitte, Hans Hasse
View PDF HTML (experimental)
Abstract:We demonstrate that thermodynamic knowledge acquired by humans can be transferred to computers so that the machine can use it to solve thermodynamic problems and produce explainable solutions with a guarantee of correctness. The actionable knowledge representation system that we have created for this purpose is called KnowTD. It is based on an ontology of thermodynamics that represents knowledge of thermodynamic theory, material properties, and thermodynamic problems. The ontology is coupled with a reasoner that sets up the problem to be solved based on user input, extracts the correct, pertinent equations from the ontology, solves the resulting mathematical problem, and returns the solution to the user, together with an explanation of how it was obtained. KnowTD is presently limited to simple thermodynamic problems, similar to those discussed in an introductory course in Engineering Thermodynamics. This covers the basic theory and working principles of thermodynamics. KnowTD is designed in a modular way and is easily extendable.
Comments: This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in the Journal of Chemical Information and Modeling, copyright \c{opyright} 2024 The Authors, published by American Chemical Society after peer review. To access the final edited and published work see this https URL
Subjects: Computational Engineering, Finance, and Science (cs.CE)
Cite as: arXiv:2407.17169 [cs.CE]
  (or arXiv:2407.17169v1 [cs.CE] for this version)
  https://doi.org/10.48550/arXiv.2407.17169
Related DOI: https://doi.org/10.1021/acs.jcim.4c00647

Submission history

From: Luisa Vollmer [view email]
[v1] Wed, 24 Jul 2024 11:21:34 UTC (323 KB)
Full-text links:

Access Paper:

license icon view license
Current browse context:
cs.CE
< prev   |   next >
new | recent | 2024-07
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)