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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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Machine Learning Methods for Multiscale Physics and Urban Engineering Problems.

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Summary
This summary is machine-generated.

This study explores multiscale physics and engineering challenges, proposing data science solutions for complex spatiotemporal problems. It highlights the need for advanced computational methods to analyze diverse data resolutions.

Keywords:
approximate Bayesian computationapproximate Hamiltoniandimension reductionhybrid approachmoist atmosphere dynamicsmolecular dynamicsmulti-resolution Gaussian Processspin icetime evolutionurban engineering

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Area of Science:

  • Multiscale Physics and Engineering
  • Data Science Applications

Background:

  • Understanding complex scientific processes and engineering ideas is crucial.
  • Multiscale problems involve concurrent, non-trivial, and coupled models across vastly different scales (space, time, energy, etc.).

Purpose of the Study:

  • To provide an overview of challenging research areas in multiscale physics and engineering.
  • To identify data science topics that can address these multiscale challenges.
  • To explore open research questions arising from analyzing multiscale data and constructing coupled models.

Main Methods:

  • Literature review of multiscale physics and engineering challenges.
  • Identification and discussion of relevant data science techniques.
  • Illustrative numeric studies on approximate Bayesian computations for multiscale data analysis.

Main Results:

  • Four key research areas in multiscale physics and engineering were identified.
  • Four potential data science approaches for addressing these challenges were proposed.
  • Demonstration of approximate Bayesian computations for analyzing multiscale data.

Conclusions:

  • Data science offers promising avenues for tackling complex multiscale spatiotemporal problems.
  • Further research is needed in analyzing data at various resolutions and developing coupled models.
  • Advanced computational techniques like approximate Bayesian computations are valuable tools for multiscale research.