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Base Quantities and Derived Quantities01:14

Base Quantities and Derived Quantities

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In any system of units, the units for some physical quantities must be specified through a measurement process. These measurements are the base quantities of the system, and their units are the base units of the system. The algebraic combinations of the base values can then be used to express all other physical quantities. Each of these physical quantities is then referred to as a derived quantity, with each unit being referred to as a derived unit.
The International Organization for...
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Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
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Application of Linearization and Approximation01:29

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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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Accuracy, limits, and approximation01:28

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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Updated: Jan 22, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Galerkin approximation of dynamical quantities using trajectory data.

Erik H Thiede1, Dimitrios Giannakis2, Aaron R Dinner1

  • 1Department of Chemistry and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA.

The Journal of Chemical Physics
|July 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for chemical mechanism analysis, improving estimates of dynamical statistics like reaction rates. The method uses Galerkin expansion and diffusion maps, offering accuracy comparable or superior to Markov state models.

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Area of Science:

  • Chemical dynamics
  • Computational chemistry
  • Statistical mechanics

Background:

  • Estimating dynamical statistics (e.g., reaction rates, committors) is crucial for understanding chemical mechanisms.
  • Current methods often rely on approximations and can be limited by model construction techniques.

Purpose of the Study:

  • To present a general framework for calculating dynamical statistics by approximating boundary value problems.
  • To introduce an alternative basis construction using diffusion maps for improved accuracy.
  • To demonstrate the benefits of delay embedding in dynamical system projection for model construction.

Main Methods:

  • Approximation of boundary value problems using dynamical operators with Galerkin expansion.
  • Construction of an alternative basis set inspired by diffusion maps.
  • Application of delay embedding for system dynamics projection.

Main Results:

  • The proposed framework provides accurate estimations of dynamical statistics.
  • The diffusion map-based basis set yields results comparable or superior to traditional Markov state models.
  • Delay embedding significantly reduces information loss, enhancing dynamical statistics estimates.

Conclusions:

  • The developed framework offers a robust and accurate approach to chemical mechanism analysis.
  • The combination of Galerkin expansion, diffusion maps, and delay embedding presents a powerful computational tool.
  • This work advances the accurate estimation of key dynamical quantities in chemical systems.