Is this pde analytically solvable?

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In summary, the conversation discusses a theoretical biologist developing a spatial model for animal movement and wondering if an analytical solution can be derived for y(x,t) based on the given structure of an equation. The function theta(x,t) plays a crucial role in determining if an analytical solution is possible.
  • #1
nigels
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I'm a theoretical biologist in the process of developing a spatial model for animal movement. So far, I've arrived at the following structure of an equation (see attachment):
*theta is just some function of x and t.

Having never formally studied pde, I'm wondering whether one can, from this, derive an analytical solution to y(x,t) and, if so, how (or, what sources can I go to to find the solution)?
 
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I don't see an attachment...
 
  • #3
hope it works this time

https://www.physicsforums.com/attachment.php?attachmentid=34793&d=1303613043
 
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  • #4
I'm wondering whether one can, from this, derive an analytical solution to y(x,t)
Analytical solution can or cannot be derived, depending the form of the function theta(x,t)
What is the function theta(x,t) ?
 
  • #5


I can understand your concern about whether this PDE is analytically solvable. In order to determine if a PDE is analytically solvable, we need to consider the properties of the equation and the boundary conditions. From the structure of the equation you have provided, it appears to be a partial differential equation with both spatial and temporal variables. The presence of a function of x and t, represented as theta, also suggests that this is a nonlinear PDE.

In general, nonlinear PDEs are more difficult to solve analytically compared to linear PDEs. However, it is not impossible to find analytical solutions for nonlinear PDEs. In order to do so, one would need to apply various mathematical techniques such as separation of variables, Fourier transforms, or numerical methods like finite difference or finite element methods.

In your case, it would be helpful to have more information about the boundary conditions and the specific form of theta to determine if an analytical solution is feasible. If you are not familiar with PDEs, I suggest consulting textbooks or online resources that cover the basics of PDEs and their solution methods. You may also consider collaborating with a mathematician or a computational biologist who has experience in solving PDEs.

In summary, while it is not possible to determine if your PDE is analytically solvable without more information, it is certainly worth exploring different solution methods to see if an analytical solution can be obtained. I wish you the best of luck with your research.
 

Related to Is this pde analytically solvable?

1. What does it mean for a pde to be analytically solvable?

A pde (partial differential equation) is considered analytically solvable if its solution can be expressed in terms of known functions such as polynomials, trigonometric functions, and exponential functions.

2. How do you determine if a pde is analytically solvable?

There is no definite method for determining if a pde is analytically solvable. However, some techniques such as separation of variables and Laplace transform can be used to solve certain types of pdes analytically.

3. Can all pdes be solved analytically?

No, not all pdes can be solved analytically. In fact, most pdes cannot be solved analytically and require numerical methods for approximation.

4. What are the advantages of solving a pde analytically?

Solving a pde analytically can provide an exact solution, which is useful for understanding the behavior of the system described by the pde. It can also provide insight into the underlying physical or mathematical principles at work.

5. Are analytically solvable pdes more important or valuable than numerically solvable ones?

Both analytically and numerically solvable pdes have their own importance and value. Analytically solvable pdes are useful for understanding the fundamental properties of a system, while numerically solvable pdes are more practical for real-world applications as they can handle a wider range of complex and nonlinear systems.

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