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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Surrogate Model Development for Digital Experiments in Welding
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Multiscale modelling: an industrial perspective.

Carlos Fonte1, Crispin Cooper1, Alessandro Abena1

  • 1Johnson Matthey plc Technology Centre , Reading, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|April 16, 2026
PubMed
Summary
This summary is machine-generated.

Multiscale modeling accelerates industrial product development by bridging atomic-level insights to macroscopic performance. This approach enhances new materials discovery and innovation in catalysis through computational simulations and experimental validation.

Keywords:
industrial catalysismachine learningmultiscale modellingquantum computingsimulations

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Engineering

Background:

  • Computational modeling is crucial in industrial materials and catalysis research.
  • Powerful algorithms and increased computational power drive advancements.

Purpose of the Study:

  • Explore multiscale modeling for accelerating product development.
  • Innovate new materials discovery using computational techniques.
  • Connect microscopic phenomena to macroscopic performance.

Main Methods:

  • Discuss multiscale modeling from atomic to continuum levels.
  • Apply methods to gain insight into real-world catalysts.
  • Integrate model predictions with experimental validation and advanced characterization.

Main Results:

  • Demonstrate how atomic/molecular understanding impacts industrial processes.
  • Highlight the essential link between computational predictions and experimental outcomes.
  • Showcase the acceleration of new industrial product design and development.

Conclusions:

  • Multiscale modeling transforms the design and development of industrial products.
  • Emerging techniques like machine learning address methodological limitations.
  • The approach effectively connects microscopic behavior to macroscopic performance.