Numerical modelling of differential problems
Introduction to numerical analysis
Formulation of a generic problem: F(x,d) = 0
- F, d known ⇒ direct problem
- F, x known ⇒ inverse problem
- x, d known ⇒ identification problem
A problem is said to be well-posed when the problem admits a solution, is unique and continuously depends on the data. If introduced a small perturbation on data, it is expected to have a small perturbation on the solution:
F(x+δx, d+δd)=0 ∀y>0 ∃K(y,d): ‖δd‖
If K is large (>1000) the problem is ill-conditioned and the solution is totally different.
If K is small (≤7-100), the problem is well-conditioned.
Numerical methods
Discrete numerical counterpart Fn(xn,dn)=0 n>1 of the continuous problem:
A method can be said consistent if:
Fn(x,d) = Fn(x,d) - F(x,d) ⇒ 0
If this property is true for every n, the method is said to be strongly consistent:
Fn(x,d) = ∀n
A method is said to be stable (or well-posed) if ∀ fixed n, there exists a unique solution xn for d and xn depends continuously on the data, similarly to the continuous problem:
Fn(xn+δxn,dn+δdn)=0
If the continuous problem is well-posed, passing to the numerical method we want to maintain this property, unless it’s useless.
A numerical method is said to be convergent to the true solution if:
∥x-xn∥ → 0 for n → ∞
Measure of convergence:
- ϵ(xn) = |x-xn|
- ϵrel(xn) = |x-xn| / |x| if x≠0
Relation between consistency, stability and convergence: Lax-Richtmeyer equivalence method ⇒ for a consistent numerical method, stability is equivalent to convergence.
Ex: -u''(x) = f(x)
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Numerical Analysis
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Esercizi preparazione Esame (base) Numerical Methods in Engineering Sciences
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Numerical Methods - Prof. Vergara
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Appunti di "Numerical Analysis for Partial Differential Equations"