Heun’s method
E300767
Heun’s method is a second-order Runge–Kutta numerical integration technique that improves on Euler’s method by using a predictor-corrector approach to achieve greater accuracy.
All labels observed (4)
| Label | Occurrences |
|---|---|
| Heun method | 1 |
| Heun’s method canonical | 1 |
| improved Euler method | 1 |
| modified Euler method | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2815530 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heun’s method Context triple: [Euler’s method for numerical integration, isGeneralizedBy, Heun’s method]
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A.
Euler’s method for numerical integration
Euler’s method for numerical integration is a simple first-order numerical procedure used to approximate solutions to ordinary differential equations by stepping forward in small increments.
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B.
Milstein method
The Milstein method is a numerical scheme for solving stochastic differential equations that improves on the Euler–Maruyama method by including derivative terms of the diffusion coefficient for higher accuracy.
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C.
Halley’s method for solving equations
Halley’s method for solving equations is an iterative numerical algorithm, related to and faster-converging than Newton’s method, used to find approximate roots of equations.
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D.
Euler–Maruyama method
The Euler–Maruyama method is a basic time-stepping scheme for numerically approximating solutions to stochastic differential equations, widely used in simulations of systems with noise such as Langevin dynamics.
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E.
Crank–Nicolson scheme
The Crank–Nicolson scheme is a finite difference method for numerically solving time-dependent partial differential equations, especially parabolic ones like the heat equation, known for its second-order accuracy and unconditional stability.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Heun’s method Target entity description: Heun’s method is a second-order Runge–Kutta numerical integration technique that improves on Euler’s method by using a predictor-corrector approach to achieve greater accuracy.
-
A.
Euler’s method for numerical integration
Euler’s method for numerical integration is a simple first-order numerical procedure used to approximate solutions to ordinary differential equations by stepping forward in small increments.
-
B.
Milstein method
The Milstein method is a numerical scheme for solving stochastic differential equations that improves on the Euler–Maruyama method by including derivative terms of the diffusion coefficient for higher accuracy.
-
C.
Halley’s method for solving equations
Halley’s method for solving equations is an iterative numerical algorithm, related to and faster-converging than Newton’s method, used to find approximate roots of equations.
-
D.
Euler–Maruyama method
The Euler–Maruyama method is a basic time-stepping scheme for numerically approximating solutions to stochastic differential equations, widely used in simulations of systems with noise such as Langevin dynamics.
-
E.
Crank–Nicolson scheme
The Crank–Nicolson scheme is a finite difference method for numerically solving time-dependent partial differential equations, especially parabolic ones like the heat equation, known for its second-order accuracy and unconditional stability.
- F. None of above. chosen
Statements (42)
| Predicate | Object |
|---|---|
| instanceOf |
Runge–Kutta method
ⓘ
numerical integration method ⓘ ordinary differential equation solver ⓘ |
| advantage | better accuracy than Euler for same step size ⓘ |
| alsoKnownAs |
explicit trapezoidal rule
ⓘ
improved Euler method ⓘ
linked to:
Heun’s method
modified Euler method ⓘ
linked to:
Heun’s method
|
| appliesTo | initial value problems for ordinary differential equations ⓘ |
| basedOn |
Euler’s method
ⓘ
linked to:
Euler’s method for numerical integration
|
| belongsToFamily | explicit two-stage Runge–Kutta methods ⓘ |
| canBeExtendedTo | adaptive step-size control ⓘ |
| category | one-step ODE integration method ⓘ |
| comparedToEuler |
achieves second-order accuracy instead of first-order
ⓘ
requires one additional function evaluation per step ⓘ |
| computesIntermediateValue | y_tilde = y_n + h f(t_n, y_n) ⓘ |
| correctorSlopeComputation | average of initial and predicted slopes ⓘ |
| disadvantage | higher computational cost per step than Euler ⓘ |
| firstStageDescription | predictor step using Euler’s method ⓘ |
| globalErrorOrder | O(h^2) ⓘ |
| goal | improve accuracy over Euler’s method ⓘ |
| implementationComplexity | simple ⓘ |
| isExplicit | true ⓘ |
| isSingleStepMethod | true ⓘ |
| localTruncationErrorOrder | O(h^3) ⓘ |
| namedAfter | Karl Heun ⓘ |
| numericalQuadratureAnalogy | trapezoidal rule for integrating derivative over a step ⓘ |
| order | second-order ⓘ |
| relatedMethod |
classical fourth-order Runge–Kutta method
ⓘ
midpoint Runge–Kutta method ⓘ
linked to:
Runge–Kutta methods
|
| requires | evaluation of right-hand side function f(t,y) ⓘ |
| secondStageDescription | corrector step using average of slopes ⓘ |
| stability | more stable than explicit Euler for many problems ⓘ |
| stageCount | 2 ⓘ |
| stepType | two-stage Runge–Kutta scheme ⓘ |
| timeStepping | fixed step size in basic form ⓘ |
| typicalUse | solving non-stiff ordinary differential equations ⓘ |
| updateFormula | y_{n+1} = y_n + h/2 [ f(t_n, y_n) + f(t_n + h, y_tilde ) ] ⓘ |
| usedIn |
applied mathematics education
ⓘ
engineering simulations ⓘ scientific computing ⓘ |
| uses | predictor–corrector approach ⓘ |
| usesSlopeEvaluationsPerStep | 2 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Heun’s method Description of subject: Heun’s method is a second-order Runge–Kutta numerical integration technique that improves on Euler’s method by using a predictor-corrector approach to achieve greater accuracy.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Heun’s method
linked to: Heun’s method
linked to: Heun’s method