Nonlinear Least Squares
Nonlinear Least Squares is a parameter estimation method that minimizes the sum of squared residuals between observed data and a nonlinear model function, distinguished from linear least squares by its reliance on iterative optimization to solve non-convex, nonlinear dependencies between parameters and predictions.
2.3K
Publications
216.3K
Citations
5.8K
Authors
1.7K
Institutions
Publications per year
2017–2026
391
Variants
Nonlinear Least Squares EstimationNonlinear Least Squares Regression
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University of North Carolina at Chapel Hill, United States
12 publications · 951 citations
12
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12
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9
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1.7K
University of California, Berkeley
Berkeley, United States
22 h-index
51 publications
4.1K citations

Langley Research Center
Hampton, United States
19 h-index
45 publications
2.4K citations
Stanford University
Stanford, United States
19 h-index
39 publications
4.9K citations
Massachusetts Institute of Technology
Cambridge, United States
18 h-index
42 publications
3.9K citations
Texas A&M University
College Station, United States
17 h-index
53 publications
1.8K citations
University of Wisconsin–Madison
Madison, United States
17 h-index
54 publications
3.6K citations
National Institute of Standards and Technology
Gaithersburg, United States
17 h-index
39 publications
3K citations
The Ohio State University
Columbus, United States
16 h-index
36 publications
2.3K citations
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49 publications · 2.8K citations
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42 publications · 2.1K citations
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36 publications · 3.3K citations
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JGR · 35 publications · 2.5K citations
24
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19 publications · 3K citations
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