Dimensional reduction of system of ODEs

This is evident in the fact that the system is nonlinear and all variables and parameters are in the positive octant of real numbers, meaning that the behavior of the system cannot be reduced to a lower dimensional plane.
  • #1
kalish1
99
0
Given a nonlinear system of eight autonomous differential equations with all variables and parameters living in the positive octant of real numbers:

$$dX_1/dt = \ldots\\
dX_2/dt = \ldots \\
\ldots \\
dX_8/dt = \ldots$$

and given that $\lim\limits_{t \to \infty} K(t) \to 0$ for $K(t) = X_3(t) + X_4(t) +X_5(t) + X_6(t) + X_7(t) + X_8(t),$

is it true that the long-term behavior of the original system will be identical to its behavior on the $X_1-X_2$ plane? And why or why not?

This question has been crossposted here: http://math.stackexchange.com/questions/1371952/dimensional-reduction-of-system-of-odes
 
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  • #2
No, the long-term behavior of the original system will not be identical to its behavior on the $X_1-X_2$ plane. This is because the other variables, $X_3$ to $X_8$, do have an effect on the system dynamics even if their values approach 0 asymptotically in the long-term. As such, they can influence the behavior of the system even if their values are small, and so the behavior of the system on the $X_1-X_2$ plane may be different from the behavior of the full system.
 

FAQ: Dimensional reduction of system of ODEs

What is dimensional reduction of a system of ODEs?

Dimensional reduction of a system of ODEs refers to the process of reducing the number of variables and equations in a system of ordinary differential equations (ODEs). This is done by identifying and eliminating redundant or unimportant variables, resulting in a simpler and more manageable system.

Why is dimensional reduction important in scientific research?

Dimensional reduction is important in scientific research because it allows for the simplification of complex systems and makes it easier to analyze and understand their behavior. This can save time and resources, and also help in identifying key variables and relationships within the system.

What are some common methods used for dimensional reduction of a system of ODEs?

Some common methods for dimensional reduction of a system of ODEs include using symmetry techniques, asymptotic analysis, and model reduction techniques such as Proper Orthogonal Decomposition (POD) and Reduced Order Modeling (ROM).

How does dimensional reduction affect the accuracy of a system of ODEs?

The effect of dimensional reduction on the accuracy of a system of ODEs depends on the specific method used and the complexity of the system. In some cases, it may result in a loss of accuracy due to the elimination of variables, while in others it may improve accuracy by removing extraneous information and focusing on the most important factors.

Can dimensional reduction be applied to all types of systems of ODEs?

Dimensional reduction can be applied to a wide range of systems of ODEs, including linear and nonlinear systems. However, the effectiveness of the methods used may vary depending on the specific characteristics of the system, such as the number of variables and their interrelationships.

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