Numerical solution of differential equationsEdexcel A-Level Further Maths: Flashcards
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Question
State the forward difference approximation to $\frac{dy}{dx}$ at $x_n$.
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- State the forward difference approximation to at .
- .
- State the central difference approximation to at .
- .
- State the approximation to at .
- .
- Give the formula for from the forward difference method.
- .
- Give the formula for from the central difference method.
- .
- Give the formula for for .
- .
- What is the step length ?
- The constant gap between successive values, .
- Why does the central difference method need two starting values?
- Each step uses , so both and are needed.
- How is found for the central difference method?
- By the forward difference method: .
- What do the initial conditions give for a second-order equation?
- and the gradient at .
- How do you find for a second-order equation?
- Combine the central difference at with the recurrence formula at , to eliminate .
- How is the percentage error calculated?
- .
- How can a numerical estimate be improved?
- Use a smaller step length, so the difference approximations are more accurate.
- What must be done when the equation contains both and ?
- Replace both by their difference approximations and collect the , and terms.
Exam questions on Numerical solution of differential equations
- The differential equation , with when , is solved numerically using the approximation with . Here and is the approximation to at .Given that , and that the exact solution is , calculate the percentage error in as an approximation to .2 marks
- The differential equation , with when , is solved numerically using the approximation with . Here , , and the value is found from the approximation .Find .2 marks
- The function satisfies , with and when . It is solved numerically with step length , where and approximates at , using and .Show that .3 marks
Written by the Exaim team, led by Shaun Daswani (Head of Upper Secondary, Improve ME Institute; MSc Financial Mathematics, Imperial College London; BSc, UCL) and Jason Daswani (operational lead, Improve ME Institute; LSE).