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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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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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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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In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
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Related Experiment Video

Updated: Jul 9, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

An approximation theory perspective on machine learning.

Hrushikesh N Mhaskar1, Efstratios Tsoukanis1, Ameya D Jagtap2

  • 1Institute of Mathematical Sciences, Claremont Graduate University, CA, 91711, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|March 19, 2026
PubMed
Summary
This summary is machine-generated.

This study reviews machine learning approximation theory, highlighting the gap between theory and practice. It explores emerging trends and proposes new directions for improving model generalization and understanding.

Keywords:
Approximation theoryLocal kernelsMachine learningNeural operatorsPhysics informed neural surrogates

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Machine Learning
  • Approximation Theory
  • Mathematical Foundations of Computer Science

Background:

  • Machine learning aims to build models that approximate unknown probability distributions from sampled data.
  • Neural networks and kernel methods are widely used for their computational efficiency, with their approximation capabilities studied for decades.
  • A disconnect exists between approximation theory and machine learning practice, impacting model generalization.

Purpose of the Study:

  • To review key ideas and emerging trends in function approximation within machine learning.
  • To analyze the gap between approximation theory and current machine learning practices.
  • To explore recent advancements in manifold approximation and classification as signal separation.

Main Methods:

  • Literature review of approximation capabilities in machine learning.
  • Discussion of shallow/deep networks, manifold approximation, neural operators, and transformers.
  • Examination of recent work on function approximation on unknown manifolds and classification as signal separation.

Main Results:

  • Identified shortcomings in the current machine learning framework regarding generalization.
  • Reviewed methods for function approximation on manifolds without explicit feature learning.
  • Summarized a novel approach viewing classification as a signal separation problem.

Conclusions:

  • Approximation theory is crucial for robust machine learning but is underutilized.
  • Emerging trends like neural operators and manifold learning offer promising avenues.
  • Further research is needed to bridge the theory-practice gap and address open problems in machine learning.