Tradition of Data-Intensive Use: The Example of Domain Thermophysics. Methods and Algorithms

No abstract is available for this paper.

• Publication year

  2025

• Venue

  Automatic Documentation and Mathematical Linguistics

• Publication date

  2025-08-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.3103/S0005105525700682
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Data-Intensive Science: Problems and Development of the Fourth Paradigm

  2024influential reference
• The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

  2024cited by this paper
• Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities.

  2023cited by this paper
• CALPHAD and the materials genome A 10 year anniversary

  2023influential reference
• Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

  2023cited by this paper
• Twofold Machine-Learning and Molecular Dynamics: A Computational Framework

  2023cited by this paper
• International Principles for the Publication and Dissemination of Scientific Data

  2022cited by this paper
• Data driven prediction of oil reservoir fluid properties

  2022cited by this paper
• The epistemological foundations of data science: a critical review

  2022cited by this paper
• A View on the Future of Applied Thermodynamics

  2022influential reference
• The NIST REFPROP Database for Highly Accurate Properties of Industrially Important Fluids

  2022cited by this paper
• Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review

  2022cited by this paper
• Machine Learning of Thermophysical Properties

  2021cited by this paper
• Physics-informed machine learning

  2021cited by this paper
• The European Project in the Materials Informatics Domain: Ontologies and Virtual Platforms

  2021cited by this paper
• Generating a Machine-learned Equation of State for Fluid Properties.

  2020cited by this paper
• Machine learning approaches for the prediction of materials properties

  2020cited by this paper
• Thermophysical Properties and Experimental and Modeling Density of Alkanol + Alkane Mixtures Using Neural Networks Developed with Differential Evolution Algorithm

  2020cited by this paper
• Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

  2020cited by this paper
• What is Data Science?

  2020cited by this paper
• Predicting physical properties of alkanes with neural networks

  2019cited by this paper
• Digitalization in Thermodynamics

  2019influential reference
• Machine learning for predicting thermodynamic properties of pure fluids and their mixtures

  2019cited by this paper
• Curation of Digital Scientific Data

  2019cited by this paper
• What Is Data Science

  2018cited by this paper
• 50 Years of Data Science

  2017cited by this paper
• The intensive use of digital data in modern natural science

  2017cited by this paper
• First-principles equation of state and shock compression predictions of warm dense hydrocarbons.

  2017cited by this paper
• Empirical multiparameter equations of state based on molecular simulation and hybrid data sets

  2016cited by this paper
• THE CALPHAD METHOD AND ITS ROLE IN MATERIAL AND PROCESS DEVELOPMENT.

  2016cited by this paper
• Thermophysical databases: From tables to interactive internet resources and cloud templates

  2015cited by this paper
• A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes.

  2015cited by this paper
• The Causal Nature of Modeling with Big Data

  2015cited by this paper
• CALPHAD, first and second generation – Birth of the materials genome

  2014cited by this paper
• Big data of materials science: critical role of the descriptor.

  2014cited by this paper
• The development of phase-based property data using the CALPHAD method and infrastructure needs

  2014cited by this paper
• An artificial neural network to calculate pure ionic liquid densities without the need for any experimental data

  2014influential reference
• Frontiers and challenges in warm dense matter

  2014cited by this paper
• Communication: Fundamental equation of state correlation with hybrid data sets.

  2013cited by this paper
• Accurate statistical associating fluid theory for chain molecules formed from Mie segments.

  2013cited by this paper
• Statistical analogues for fundamental equation of state derivatives

  2012influential reference
• Quantitative structure-property relationship modeling of diverse materials properties.

  2012cited by this paper
• Prediction of thermophysical properties of mixed refrigerants using artificial neural network

  2011cited by this paper
• Direct molecular NVT simulation of the isobaric heat capacity, speed of sound and Joule–Thomson coefficient

  2011influential reference
• Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction.

  2010influential reference
• Chapter 12:Multi-parameter Equations of State for Pure Fluids and Mixtures

  2010influential reference
• The Fourth Paradigm: Data-Intensive Scientific Discovery

  2009cited by this paper
• The logical structure of physicochemical data: Problems of numerical data standardization and exchange

  2008cited by this paper
• Equation of state of a real gas

  2004influential reference
• Support-vector networks

  2004cited by this paper
• Equations of State for Technical Applications. II. Results for Nonpolar Fluids

  2003cited by this paper
• Equations of State for Technical Applications. III. Results for Polar Fluids

  2003cited by this paper
• Equations of State for Technical Applications. I. Simultaneously Optimized Functional Forms for Nonpolar and Polar Fluids

  2003influential reference
• ThermoML-An XML-based approach for storage and exchange of experimental and critically evaluated thermophysical and thermochemical property data. 2. Uncertainties

  2003cited by this paper
• Multiparameter equations of state — recent trends and future challenges

  2001influential reference
• Potential applications of artificial neural networks to thermodynamics: vapor–liquid equilibrium predictions

  1999cited by this paper
• Quantum-Chemical Descriptors in QSAR/QSPR Studies.

  1996cited by this paper
• Thermodynamically Consistent Interpolation for Equation of State Tables

  1996cited by this paper
• PVT data analysis using neural network models

  1993cited by this paper
• Fluid property predictions with the aid of neural networks

  1993cited by this paper
• A new method for optimizing the structure of thermodynamic correlation equations

  1989influential reference
• Methods of evaluating reliability in reference data on the properties of substances and materials

  1988cited by this paper
• ESTIMATION OF PURE-COMPONENT PROPERTIES FROM GROUP-CONTRIBUTIONS

  1987cited by this paper
• The determination of equations of state for nitrogen and oxygen

  1973cited by this paper
• Thermodynamic Properties of Nitrogen Including Liquid and Vapor Phases from 63K to 2000K with Pressures to 10,000 Bar,

  1973cited by this paper
• Curve-fitting techniques and applications to thermodynamics

  1967cited by this paper
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters

  1963cited by this paper
• The Future of Data Analysis

  1962influential reference
• The properties of gases and liquids

  1959influential reference
• Equations of State.

  1957cited by this paper
• The Martensitic Transformation in the Iron-Nickel System

  1956cited by this paper
• Evaluation of Equation of State Constants with Digital Computers

  1951cited by this paper
• An Empirical Equation for Thermodynamic Properties of Light Hydrocarbons and Their Mixtures I. Methane, Ethane, Propane and n‐Butane

  1940cited by this paper
• thermodynamics: Connecting property

  year unknowncited by this paper

• No citing papers are available for this paper.