Langevin Curve Fitting for Magnetization and Applied Field Relationship

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Discussion Overview

The discussion revolves around the application of curve fitting algorithms to model the relationship between magnetization (M) and applied field (H) using the Langevin function. Participants explore the mathematical formulation and seek guidance on implementing a fitting algorithm, specifically in the context of a log-normal distribution for the weighting function W(x_i).

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Homework-related

Main Points Raised

  • One participant presents a mathematical model for magnetization as a function of applied field and temperature, incorporating a log-normal distribution for W(x_i).
  • Another participant requests clarification on the data available and the meaning of W(x_i)(x_i), questioning whether x_i is a random number and if the goal is to determine the constant A from a dataset.
  • A suggestion is made regarding the use of non-linear least squares minimization for fitting the model, with a reference to MATLAB as a potential tool.
  • One participant expresses interest in learning about nonlinear least squares optimization.
  • A specific method, the Levenberg-Marquardt method, is recommended for optimization.

Areas of Agreement / Disagreement

The discussion includes multiple viewpoints on the approach to curve fitting and the specifics of the mathematical model, with no consensus reached on the best method or the interpretation of certain variables.

Contextual Notes

Participants have not fully defined the assumptions regarding the data or the nature of the variables involved, leading to some ambiguity in the discussion.

Who May Find This Useful

Researchers or students interested in magnetization modeling, curve fitting techniques, and optimization methods in a physics or engineering context may find this discussion relevant.

mhill
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hi, there my question is let's suppose we have the magnetization (M) versus the applied field (H) as

[tex]M(H,T)= \sum _{n=1}^{N} W(x_i ) (x_i ) Lang (H.A.x_{i}/T)[/tex]

here 'A' is a constant 'T' is the temperature of system Lang(x) is the Langevin function coth(x)-1/x ,

My problem is how to use a curve fitting algorithm to solve the problem ,i am working under the assumption that [tex]W(x_i)[/tex] i=1,2,3,...,N is a log normal distribution depending only on the value x_i

my curve fitting program is just 'Origin 7' i need the algorithm to curve-fitting to a certain given distribution W(x) thanks.
 
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What data do you have? Next, what does [tex]W(x_i ) (x_i )[/tex] mean? Could you give it analytical? Do you want to find A, and have a set of data like M_i=M(H_i,T_i) where _i is an index of your data points? Is x_i a random number or what?

I know how to find A for a problem of the type M_i=M(H_i,T_i) within MATLAB but that maby not help you, but the idea is based on non-linear least square minimization.
 
thank you, if possible where could i learn 'Nonlinear least squares optimization' ??
 
Levenberg-Marquardt method... try looking for that.
 

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