Variational calculus Euler lagrange Equation

In summary, the discussion was about understanding an example from the textbook "Applied Finite Element Analysis" on the use of variational calculus, specifically the Euler-Lagrange equation. The last part of the derivation involves the appearance of the term 1/2, which may seem to come out of nowhere. However, this is simply a result of applying the operational properties of differentiation to functions and functionals. This can be seen by performing the calculations without using the notation of variations.
  • #1
hash054
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I am trying to understand an example from my textbook "applied finite element analysis" and in the variational calculus, Euler lagrange equation example I can't seem to understand the following derivation in one of its examples
∫((dT/dx)(d(δT)/dx))dx= ∫((dT/dx)δ(dT/dx))dx= ∫((1/2)δ(dT/dx)^2)dx

limits from 0 to 5.

My question here is how in the last part of the derivation 1/2 appears out of the blue where the integratal remains intact..
If anyone know the answer.. kindly refer me some examples also
 
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  • #2
essentially it is a restatement of: xdx = 1/2 d(x^2)
 
  • #3
So this is a reverse step?
 
  • #4
Hi All,
I found it easier to master the functional derivative concept by performing the calculations out, leaving aside for a moment the symbol $$\delta$$ to denote variations, a useful notation that might though hide the mechanics of what is going on.
Then your example becomes transparent: the functional derivative of the functional $$\int_{\Omega} y'^{2} \mathrm{d}\Omega$$ in the direction of the test function $$\mu$$ (in other terms, introducing a variation $$\hat{y} = y(x) + \epsilon \mu (x)$$ according to the definition equals
$$lim_{\epsilon \to 0}\frac {\int_{\Omega} [\hat{y}'^2 - y'^ 2] \mathrm{d}\Omega}{\epsilon}$$
$$lim_{\epsilon \to 0}\frac {\int_{\Omega} [{y'^{2} + 2\epsilon \mu' y'+\epsilon^2 \mu^{2}} - y' ^2] \mathrm{d}\Omega}{\epsilon}$$
some terms cancel, and you are left with the result you wnated to confirm: it turns out the differentiation of functions and functional share common operational properties, such as xdx = 1/2 d(x^2).
 

1. What is the Euler-Lagrange equation?

The Euler-Lagrange equation is a mathematical equation that is used in the study of variational calculus. It is named after the mathematicians Leonhard Euler and Joseph-Louis Lagrange, who first developed it. This equation is used to find the function that minimizes a given functional, which is a mathematical expression involving a function and its derivatives.

2. How is the Euler-Lagrange equation derived?

The Euler-Lagrange equation is derived by setting the functional's first variation to zero. The first variation is the change in the value of the functional when the function is varied by a small amount. By setting this variation to zero, we can find the critical points of the functional, which are the points where the function minimizes the functional.

3. What is the significance of the Euler-Lagrange equation in physics?

The Euler-Lagrange equation has significant applications in physics, particularly in classical mechanics and field theory. It is used to derive the equations of motion for a system by minimizing the action, which is a functional that represents the total energy of the system. This allows us to understand the behavior of physical systems and make predictions about their motion.

4. Can the Euler-Lagrange equation be applied to any type of functional?

Yes, the Euler-Lagrange equation can be applied to any type of functional, as long as it is continuous and differentiable. This includes both single-variable and multi-variable functions. However, the functional must also satisfy certain boundary conditions for the Euler-Lagrange equation to be applicable.

5. What are some real-world applications of the Euler-Lagrange equation?

The Euler-Lagrange equation has various applications in different fields, such as economics, engineering, and computer science. In economics, it is used to find the optimal path for a consumer to maximize their utility. In engineering, it is used to find the optimal design for a structure that minimizes its energy or cost. In computer science, it is used in machine learning algorithms to minimize the error between predicted and actual data. It also has applications in optimal control theory and image processing.

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