Basis functions and spanning a solution space

In summary, the conversation discusses the process of solving an eigenvalue problem for a linear differential operator. The approach involves using basis functions and a variational procedure to approximate the eigenvalues. However, the initial solution does not give correct eigenvalues, leading to the discovery that the basis functions must be split into even and odd components. This approach ultimately yields the correct solutions for the eigenvalue problem. The conversation also briefly mentions a more complex problem involving hydrodynamic equations of motion in a 2D channel.
  • #1
member 428835
Hi PF

Given some linear differential operator ##L##, I'm trying to solve the eigenvalue problem ##L(u) = \lambda u##. Given basis functions, call them ##\phi_i##, I use a variational procedure and the Ritz method to approximate ##\lambda## via the associated weak formulation
$$\langle L(\phi_i),\phi_j\rangle = \lambda \langle \phi_i,\phi_j\rangle.$$

As you can see, this expression is now a matrix equation, solutions to which are straightforward. For my particular problem, the basis functions are $$\phi_j = \cos\left( \frac{\pi j}{2}(x+1) \right) \cosh\left( \frac{\pi j}{2}(y+h) \right).$$

However, this solution, when inputted into the weak formulation equation, does not output correct eigenvalues. However, ##\phi_j## can be split into even and odd components:
$$ \phi_j^o = \sin \left( \pi(j-1/2)x \right)\cosh\left( \pi(p-1/2)(y+h) \right)\\
\phi_j^e = \cos \left( \pi j x \right)\cosh\left( \pi j(y+h) \right)
$$

Now to obtain eigenvalues I solve two separate equations, one for even eigenvalues and one for odd:
$$\langle L(\phi_i^e),\phi_j^e\rangle = \lambda \langle \phi_i^e,\phi_j^e\rangle\\
\langle L(\phi_i^o),\phi_j^o\rangle = \lambda \langle \phi_i^o,\phi_j^o\rangle.$$

This latter approach gives correct solutions: why? Any insight or direction is greatly appreciated.
 
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  • #2
Please be more specific. Your post seems to suggest that you have some particular linear differential operator in mind. Also, please show what you have done more explicitly and be more explicit in what you obtain.
 
  • #3
Orodruin said:
Please be more specific. Your post seems to suggest that you have some particular linear differential operator in mind. Also, please show what you have done more explicitly and be more explicit in what you obtain.
The real problem I'm worried is kind of long, tough to explain, and could scare off anyone who could potentially help me (posted it in a previous thread and everyone ran :cry:, and I don't blame them). I tried finding the post but couldn't.

So I'll briefly summarize: fluid rests in a 2D channel of domain ##\Omega##, the gas-fluid interface ##\Gamma##, fluid contact line ##\gamma##, and channel wall and bottom ##\Sigma##. Center depth is ##h##. The governing hydrodynamic equations of motion are then
$$
\nabla^2 \phi = 0 \,\,\,\,\,[\Omega]\\

\phi_n = 0 \,\,\,\,\,[\Sigma]\\

\pm\phi' + \cos a \cot a \phi = 0 \,\,\,\,\, [\gamma]\\

\int_\Gamma \phi_n = 0 \,\,\,\,\, [\Gamma]\\

-d_s^2\phi_n - c^2 \phi_n = \lambda \phi \,\,\,\,\,[\Gamma].
$$

My basis functions in my first post solve the first 2 equations analytically. Equation 5 is the eigenvalue problem, where ##L \phi_n \equiv -d_s^2\phi_n - c^2 \phi_n##, and I solve this using inverse differential operators. I build equations 3 and 4 into those operators. Subscripted ##n## denotes a normal derivative to the given geometric structure.

To relate all this to my initial post, equation 5 is the eigenvalue problem I approximately solve. The basis functions in the first post exactly solve the first 2 equations (easy to verify). It's confusing that this problem only admits solutions when working with even and odd functions separately, which is my question.
 

FAQ: Basis functions and spanning a solution space

1. What are basis functions?

Basis functions are mathematical functions that are used to represent other functions or data points. They can be used to decompose a complex function into simpler components, making it easier to analyze and manipulate.

2. How do basis functions span a solution space?

Basis functions span a solution space by providing a set of functions that can be combined in different ways to represent any function within that space. This allows for a more efficient and flexible way to find solutions to complex problems.

3. What is the importance of spanning a solution space?

Spanning a solution space is important because it allows for a more comprehensive and accurate representation of functions or data points. It also enables the use of different basis functions to find the best fit for a particular problem, leading to more efficient and effective solutions.

4. How are basis functions used in machine learning?

In machine learning, basis functions are often used to transform the input data into a higher-dimensional space, making it easier for the algorithm to find patterns and make predictions. They can also be used to regularize and simplify complex models.

5. What are some common types of basis functions?

Some common types of basis functions include polynomial functions, Fourier series, and wavelet functions. Other types include radial basis functions, spline functions, and Gaussian functions. The choice of basis functions depends on the specific problem and the desired outcome.

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