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Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
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Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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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Transmission-Line Differential Equations01:26

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Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Updated: Dec 2, 2025

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
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Partial differential equation models in macroeconomics.

Yves Achdou1, Francisco J Buera2, Jean-Michel Lasry3

  • 1Université Paris Diderot, Sorbonne Paris Cité, Laboratoire Jacques-Louis Lions, Paris, France.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|October 8, 2014
PubMed
Summary
This summary is machine-generated.

Mathematicians can explore challenging partial differential equations (PDEs) from economics. This research highlights complex PDEs in macroeconomics, offering new avenues for mathematical study and open questions.

Keywords:
firm size distributionheterogeneous agentsincome and wealth distributionmean field games

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Area of Science:

  • Economics and Mathematics
  • Mathematical Economics
  • Applied Mathematics

Background:

  • Macroeconomic models address critical economic questions.
  • These models generate complex partial differential equations (PDEs).
  • The mathematical structure of these PDEs presents significant challenges.

Approach:

  • Introduce and exemplify PDEs arising in macroeconomics.
  • Review existing mathematical knowledge on their properties.
  • Identify and present open research questions.

Key Points:

  • Partial differential equations (PDEs) from macroeconomics offer rich mathematical problems.
  • The complexity of these economic PDEs is a key feature.
  • Understanding these PDEs can advance both economics and mathematics.

Conclusions:

  • Encourage mathematicians to investigate these challenging PDEs.
  • Highlight the interdisciplinary potential for advancing economic theory and mathematical understanding.
  • Outline future research directions in this specialized field.