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Glossary Glossary

unknown function.
In a differential equation, the unknown function is the quantity you are solving for, also called the dependent variable.
antiderivative.
The antiderivative of \(f(x)\) is a function whose derivative is \(f(x)\text{.}\) It is \(y\) in the equation \(y' = f(x)\text{.}\)
differential equation (DE).
A differential equation is an equation involving one or more derivatives of an unknown function or dependent variable.
ordinary differential equation (ODE).
An ordinary differential equation is a differential equation whose unknown function depends on a single variable.
partial differential equation (PDE).
A partial differential equation is a differential equation whose unknown function depends on more than one variable.
dependent variable.
The dependent variable is the unknown function being solved for in a DE. It is the variable with derivatives applied to it.
\begin{equation*} -2\frac{d{\DLO y}}{dt} + 4t{\DLO\underline{y}} = t^2 \quad\text{dependent variable:}\ {\DLO\underline{y}} \end{equation*}
independent variable.
The independent variable is the variable that the dependent variable is a function of. The derivatives in a DE are taken with respect to the independent variable.
\begin{equation*} -2\frac{dy}{d{\DLO\underline{t}}} + 4{\DLO\underline{t}}y = {\DLO\underline{t}}^2 \quad\text{independent variable:}\ {\DLO\underline{t}} \end{equation*}
term.
A term is any part of a DE separated by addition or subtraction. Terms are identified by the dependent variable it contains. If a term does not contain the dependent variable, it is called a free term.
\begin{equation*} {\DLO\underset{y'\ \text{term}}{\underline{-2y'}}} + {\DLO\underset{y\ \text{term}}{\underline{4ty}}} = {\DLO\underset{\text{free term}}{\underline{t^2+1}}} \end{equation*}
free term.
A free term is a term in a DE that does not involve the dependent variable or any of its derivatives.
\begin{equation*} -2y' + 4ty = {\DLO\underline{t^2}} \quad\text{free term:}\ {\DLO\underline{t^2}} \end{equation*}
coefficient.
A coefficient is a constant or a function of the independent variable that multiplies the dependent variable or one of its derivatives.
\begin{equation*} {\DLO\underline{-2}}y' + {\DLO\underline{4t}}y = t^2 \quad\text{coefficients:}\ {\DLO\underline{-2}}, {\DLO\underline{4t}} \end{equation*}
order.
The highest derivative of the dependent variable that appears in a DE. Powers of the variable do not affect the order.
\begin{align*} \text{3rd Order:}\quad \amp t^2y'' + {\DLO\underline{y'''}} = 0\\ \text{2nd Order:}\quad \amp {\DLO\underline{\frac{d^2y}{dt^2}}} + 3\left(\frac{dy}{dt}\right)^4 = t\\ \text{5th Order:}\quad \amp {\DLO\underline{y^{(5)}}} - y' + y^8 = \frac{1}{t} \end{align*}
linear term.
A term in which the dependent variable, \(y\text{,}\) or one of its derivatives appears by itself and to the first power.
\begin{equation*} \text{linear terms}:\quad 2ty'',\quad e^t y,\quad \frac{1}{t}y',\quad 7t \end{equation*}
nonlinear term.
A term that is not linear, such as one where the dependent variable or its derivatives are multiplied together, raised to a power other than one, or placed inside a nonlinear function.
\begin{equation*} \text{nonlinear terms}:\quad y^2,\quad y\,y''',\quad \sin(y') \end{equation*}
linear differential equation.
\begin{equation*} y'' + 4y' + 7y = \cos(t),\quad t^2y'' - e^t y' + \frac{1}{t}y = 17t,\quad y''' + \frac{1}{t}y' = 0 \end{equation*}
nonlinear differential equation.
A differential equation (DE) that contains at least one nonlinear term.
\begin{equation*} y'' + {\DLO\underline{7y^2}} = \cos(t),\quad {\DLO\underline{y''y'}} + {\DLO\underline{\frac{t}{y}}} = 17t,\quad y''' + \frac{1}{t}y' = {\DLO\underline{\sin(y)}} \end{equation*}
linear combination.
A sum of linear terms. For example, the equation
\begin{equation*} t^2y'' + 2ty' + 4y \end{equation*}
is a linear combination of the \(y''\text{,}\) \(y'\text{,}\) and \(y\) terms.
forcing function (forcing term).
The free term, \(f(t)\text{,}\) in a linear differential equation.
\begin{equation*} y'' + 4y = {\DLO\underline{\cos(t)}} \quad\text{forcing function:}\ {\DLO\underline{\cos(t)}} \end{equation*}
satisfies (a differential equation).
A function satisfies a DE when substituting the function and any needed derivatives into the equation makes both sides simplify to the same expression. For example, \(y=2x^2\) satisfies \(xy'-2x^2=y\) since
left-hand
\(xy' - 2x^2\)
side
\(x(4x) - 2x^2\)
\(y\)
right-hand
\({\DLO 2x^2}\)
\({\DLO \leftarrow \text{equal} \rightarrow}\)
\({\DLO 2x^2}\)
side
solution (to a differential equation).
A solution to a DE is a function that satisfies the equation. For example, since \(y=2x^2\) satisfies \(xy'-2x^2=y\text{,}\) it is a solution.
verifying (a solution to a differential equation).
Verifying that a function, \(y=f(x)\text{,}\) is a solution to a DE is the process of showing that \(y=f(x)\) satisfies the DE.
general solution.
A general solution of a DE is the general form that all solutions share up to one or more arbitrary constants. For example, \(y=ce^{2x}\) is the general solution for \(y'-2y=0\text{.}\)
particular solution.
A particular solution is a specific function obtained from a general solution by finding or selecting values for its constants. For example, if \(y=ce^{2x}\) is a general solution, then the following are particular solutions:
\(c={\DLO -5} \to\)
\(y={\DLO -5}e^{2x}\)
\(c={\DLO 0} \to\)
\(y={\DLO 0}\)
\(c={\DLO 3} \to\)
\(y={\DLO 3}e^{2x}\)
family of solutions.
A family of solutions is the complete collection of all particualr solutions described by a general solution. For example, the family generated by \(y=ce^{2x}\) contains \(y=e^{2x}\text{,}\) \(y=3e^{2x}\text{,}\) and infinitely many other particular solutions corresponding to different values of \(c\text{.}\)
initial condition.
An initial condition is a known value of a solution or one of its derivatives at a specific input value, used to determine the constants in a general solution. For example, \(y(0)=2\) and \(h'(0)=0\) are initial conditions.
initial-value problem (IVP).
An initial-value problem pairs a DE with enough initial conditions to determine a particular solution. For example,
\begin{equation*} h''(t)=-32,\ h(0)=100,\ h'(0)=0 \end{equation*}
is an IVP because it combines a DE with initial conditions at \(t=0\text{.}\)
solution curve.
A solution curve is the graph of a particular solution. In a family such as \(y=ce^{x^2}+3\text{,}\) each value of \(c\) gives a different curve, and the curve through a chosen initial point is the particular solution for that initial condition.
constant of integration.
A constant added when finding an antiderivative or solving a DE by integration. It accounts for the fact that many different functions have the same derivative. For example, integrating \(y'=2x\) gives \(y=x^2+c\text{,}\) where \(c\) is the constant of integration.
isolate the derivative.
To isolate the derivative is to rewrite a DE so the derivative term is alone on one side of the equation. For example, \(y'+18xy=6x\) becomes \(y'=6x-18xy\text{.}\)
direct integration.
Refers to isolating a single derivative in a DE and integrating both sides. For example, from
\begin{equation*} \frac{d}{dx}\left[e^{2x}y\right]=4e^{2x} \end{equation*}
we integrate and solve to get:
\begin{equation*} e^{2x}y=2e^{2x}+c \quad\Rightarrow\quad y=2+c e^{-2x}\text{.} \end{equation*}
separable form.
Separable form is the product form
\begin{equation*} \dfrac{dy}{dx}=f(x)\cdot g(y) \end{equation*}
where one factor depends only on the independent variable and the other depends only on the dependent variable. For example,
\begin{equation*} \frac{dy}{dx}=x(1-y) \end{equation*}
is in separable form because the \(x\)-part and \(y\)-part appear as a product.
separable (differential equation).
A first-order DE is separable if it can be rewritten in separable form.
separation of variables (SOV) method.
The separation of variables method is a procedure for solving a separable DE where we verify the equation is separable, separate the variables onto opposite sides, integrate both sides, and solve for the dependent variable if possible. For example,
\begin{equation*} \frac{dy}{dx}=5x^4\cdot\frac{1}{2y} \quad\Rightarrow\quad 2y\,dy=5x^4\,dx \quad\Rightarrow\quad y^2=x^5+c\text{.} \end{equation*}
implicit solution.
An implicit solution is a general solution or particular solution written as a relation involving the dependent variable, rather than with the dependent variable solved for explicitly. For example, \(y^2=2\sin(x)+c\) is implicit because \(y\) has not yet been isolated.
product rule.
\begin{equation*} \frac{d}{dx}[f(x)g(x)] = f(x)g'(x) + f'(x)g(x)\text{.} \end{equation*}
reversed product rule.
A reversed product rule recognizes a sum in the form
\begin{equation*} f(x)g'(x)+f'(x)g(x) \end{equation*}
as the derivative \(\frac{d}{dx}[f(x)g(x)]\text{.}\) For example,
\begin{equation*} e^{2x}y' + 2e^{2x}y = \frac{d}{dx}[e^{2x}y]\text{.} \end{equation*}
first-order linear differential equation.
\begin{equation*} A(x)y' + B(x)y = C(x)\text{,} \end{equation*}
which can also be written in standard form.
standard form (first-order linear).
\begin{equation*} y' + P(x)y = Q(x)\text{,} \end{equation*}
where \(P(x)\) and \(Q(x)\) are known functions of the independent variable. For example, dividing
\begin{equation*} 3y' = 12 - 6y \end{equation*}
by \(3\) and rearranging gives the standard form \(y' + 2y = 4\text{.}\)
complete product rule.
A complete product rule is a two-term expression that matches the pattern \(f(x)g'(x)+f'(x)g(x)\text{,}\) so it can be grouped as a single derivative using the reversed product rule. For example,
\begin{equation*} e^{2x}y' + 2e^{2x}y \end{equation*}
is a complete product rule because it equals \(\frac{d}{dx}[e^{2x}y]\text{.}\)
integrating factor.
An integrating factor is a nonzero function multiplied onto a standard form equation so that the left-hand side becomes a complete product rule. For \(y' + P(x)y = Q(x)\text{,}\) the integrating factor is
\begin{equation*} \mu(x)=e^{\int P(x)\,dx}\text{.} \end{equation*}
For example,
\begin{equation*} y' + 2y = 4 \quad\to\quad \mu(x) = e^{\int 2\,dx} = e^{2x}\text{.} \end{equation*}
integrating factor method.
The integrating factor method solves a first-order linear differential equation, such as \(3y' = 12 - 6y\) by
Rewriting it in standard form: \(y' + 2y = 4\)
Multiplying by an integrating factor: \(e^{2x}(y' + 2y) = e^{2x}\cdot 4\)
A complete product rule on the left: \([e^{2x}y]' = 4e^{2x}\)
Integration: \(e^{2x}y=2e^{2x}+c\)
Solving for \(y\text{:}\) \(y=2+c e^{-2x}\)
slope field.
A picture of the slopes of solution curves for the first-order DE
\begin{gather*} \frac{dy}{dt} = f(t,y) \end{gather*}
formed by placing a short segment at each point \((t,y)\) with slope \(f(t,y)\text{.}\)
autonomous differential equation.
A first-order DE whose slope depends only on the dependent variable:
\begin{gather*} \frac{dy}{dt} = f(y) \end{gather*}
Its slope field has the same slope along each horizontal line.
equilibrium solution.
A constant solution of an autonomous differential equation. For a constant solution \(y=c\) of
\begin{gather*} \frac{dy}{dt} = f(y) \end{gather*}
we must have
\begin{gather*} 0 = f(c) \end{gather*}
so every value \(c\) with \(f(c)=0\) gives an equilibrium solution \(y(t)=c\text{.}\)
phase line.
A simplified diagram for an autonomous differential equation that shows only the \(y\)-axis, marks equilibria, and uses arrows to show whether solutions move up or down in each interval.
sink (stable equilibrium).
An equilibrium solution that attracts nearby solutions from both sides. On a phase line, the arrows point toward the equilibrium.
source (unstable equilibrium).
An equilibrium solution that repels nearby solutions from both sides. On a phase line, the arrows point away from the equilibrium.
semi-stable equilibrium (node).
An equilibrium solution that attracts nearby solutions on one side and repels them on the other.
linearization method.
A test for classifying an equilibrium solution \(y=c\) of
\begin{gather*} \frac{dy}{dt} = f(y) \end{gather*}
by checking the sign of \(f'(c)\text{:}\)
\begin{gather*} f'(c) < 0 \Rightarrow \text{stable equilibrium (sink)}\\ f'(c) > 0 \Rightarrow \text{unstable equilibrium (source)} \end{gather*}
If \(f'(c)=0\text{,}\) the test is inconclusive.
parameter.
A constant in a model that can be varied to study how the system changes. In
\begin{gather*} \frac{dy}{dt}=f(y,\mu) \end{gather*}
the quantity \(\mu\) is a parameter.
parameter analysis.
The study of how a model’s equilibria, stability, or long-term behavior depend on one or more parameter values.
bifurcation.
A qualitative change in a system’s dynamics caused by varying a parameter, often through a change in the number or stability of equilibria.
saddle-node bifurcation.
A bifurcation in which two equilibria collide and appear or disappear as a parameter passes through a critical value. A standard example is
\begin{gather*} \frac{dx}{dt} = \mu - x^2 \end{gather*}
where equilibria meet at \(\mu=0\text{.}\)
bifurcation diagram.
A graph of equilibrium values versus a parameter that shows where equilibria exist and whether they are stable or unstable.
logistic model.
A population model with limited resources:
\begin{gather*} \frac{dP}{dt}=rP\left(1-\frac{P}{K}\right) \end{gather*}
where \(r\) is the growth rate and \(K\) is the carrying capacity.
carrying capacity.
The largest population an environment can sustain long term. In the logistic model, it is the equilibrium value \(P=K\text{.}\)
modified logistic model.
A variation of the logistic model that also models decline at very low populations:
\begin{gather*} \frac{dS}{dt}=kS\left(1-\frac{S}{N}\right)\left(\frac{S}{M}-1\right) \end{gather*}
where \(S\) is the population. It has equilibria at \(S=0\text{,}\) \(S=M\text{,}\) and \(S=N\text{.}\)
sparsity threshold.
In the modified logistic model, the critical population level \(S=M\) below which the population decreases instead of grows.
analytic solution (closed-form solution).
A solution (to a differential equation) written as a formula that gives the exact value of the dependent variable for each input in an interval. For example,
\begin{equation*} y(t)=e^t \end{equation*}
is the analytic solution of the IVP \(y'=y,\ y(0)=1\text{.}\)
numerical method.
A procedure for building approximate values of a solution step by step instead of finding an analytic solution (closed-form solution). Euler’s method is a numerical method.
approximation.
A value or point that is close to the exact solution but not exact. In numerical work, an approximation is used to estimate a solution value, such as
\begin{equation*} y(0.65)\approx 1.916\text{.} \end{equation*}
numerical solution.
A list or table of approximations to a solution at selected input values, produced by a numerical method. For example,
\begin{equation*} (0,1),\ (0.25,1.25),\ (0.5,1.5625),\ \ldots \end{equation*}
is a numerical solution to an IVP.
iteration.
One repeated application of the basic step in a numerical method. In Euler’s method, each iteration uses the current point to compute the next approximation point.
step size.
In Euler’s method, the step size is the fixed distance \(h\) between consecutive input values in the approximation:
\begin{equation*} t_{k+1}=t_k+h\text{.} \end{equation*}
Euler’s method.
A numerical method for approximating the solution to an IVP by following the slope at the current point. For
\begin{equation*} y'=f(t,y),\qquad y(t_0)=y_0 \end{equation*}
Euler’s method uses the update rule
\begin{equation*} y_{k+1}=y_k+h\,f(t_k,y_k) \end{equation*}
together with \(t_{k+1}=t_k+h\text{.}\)
like terms.
In differential equation work, like terms are expressions with the same variable or function part that differ only by a constant coefficient. They can be combined by addition or subtraction.
\begin{equation*} 5e^{3x} - 2e^{3x} = 3e^{3x} \end{equation*}
constant coefficients.
A linear differential equation has constant coefficients if the coefficients of \(y\) and its derivatives are constants rather than functions of the independent variable.
\begin{equation*} 2y'' - 3y' + 5y = 0 \end{equation*}
homogeneous (linear differential equation).
\begin{equation*} y'' - 3y' + 2y = 0 \end{equation*}
nonhomogeneous (linear differential equation).
A linear differential equation is nonhomogeneous if its forcing function (forcing term) is nonzero.
\begin{equation*} y'' - 3y' + 2y = e^x \end{equation*}
linear homogeneous constant coefficient (LHCC) differential equation.
A linear differential equation that is both homogeneous and has constant coefficients, so each \(a_k\) below is a constant.
\begin{equation*} a_n y^{(n)} + a_{n-1} y^{(n-1)} + \cdots + a_1 y' + a_0 y = 0 \end{equation*}
characteristic equation.
The polynomial equation obtained from an LHCC equation by assuming an exponential solution, which reduces the differential equation to a polynomial in \(r\text{.}\)
\begin{equation*} y=e^{rx}, \quad ay'' + by' + cy = 0 \quad\Rightarrow\quad ar^2 + br + c = 0 \end{equation*}
fundamental solutions.
The basic solution functions corresponding to the roots of the characteristic equation, used to build the general solution of an LHCC equation.
\begin{equation*} r_1 = 2,\ r_2 = -1 \quad\Rightarrow\quad e^{2x},\ e^{-x} \end{equation*}
superposition principle.
If \(y_1\) and \(y_2\) are solutions of the same homogeneous linear differential equation, then every linear combination of them is also a solution.
\begin{equation*} y_1,\ y_2 \text{ solve } ay'' + by' + cy = 0 \quad\Rightarrow\quad c_1y_1 + c_2y_2 \text{ also solves it} \end{equation*}
linear dependence.
A set of solutions is linearly dependent on an interval if there exist constants (not all zero) such that a linear combination of the solutions equals zero.
\begin{equation*} 1\bigl(3e^{2x}\bigr) - 3\bigl(e^{2x}\bigr) = 0 \end{equation*}
linear independence.
A set of solutions is linearly independent on an interval if the only linear combination that equals zero is the one with all coefficients equal to zero.
\begin{equation*} c_1 e^{2x} + c_2 e^{-x} = 0 \Rightarrow c_1 = c_2 = 0 \end{equation*}
repeated root.
A repeated root is a root of the characteristic equation that appears more than once, indicated by an exponent greater than \(1\) in the factored form of the characteristic polynomial.
\begin{equation*} (r-3)^2 = 0 \quad\text{has a repeated root } r=3 \end{equation*}
multiplicity (of a root).
The multiplicity of a root is the number of times that root occurs in the characteristic equation.
\begin{equation*} (r+1)^3 = 0 \quad\text{gives } r=-1 \text{ with multiplicity } 3 \end{equation*}
complex conjugate roots.
A pair of roots of the form \(\alpha \pm i\beta\text{.}\) For an LHCC equation, they contribute
\begin{equation*} e^{\alpha x}\bigl(c_1\cos(\beta x) + c_2\sin(\beta x)\bigr) \end{equation*}
characteristic polynomial.
The polynomial on the left side of a characteristic equation. Its roots determine the form of the general solution.
\begin{equation*} y^{(4)} - 5y'' + 4y = 0 \quad\Rightarrow\quad r^4 - 5r^2 + 4 \end{equation*}
rational root theorem.
For a polynomial with integer coefficients, any rational root \(\frac{p}{q}\) in lowest terms must have \(p\) dividing the constant term and \(q\) dividing the leading coefficient. This helps narrow the possible rational roots of a characteristic polynomial.
\begin{equation*} 2r^2 - 5r + 3 = 0 \Rightarrow \text{candidate rational roots are } \pm 1,\ \pm 3,\ \pm \frac{1}{2},\ \pm \frac{3}{2} \end{equation*}
linear nonhomogeneous constant coefficient (LNCC) differential equation.
\begin{equation*} a_n y^{(n)} + a_{n-1} y^{(n-1)} + \cdots + a_1 y' + a_0 y = f(x) \end{equation*}
homogeneous solution.
For an LNCC equation, the homogeneous solution, \(y_h\text{,}\) solves the associated homogeneous equation
\begin{equation*} a_n y^{(n)} + a_{n-1} y^{(n-1)} + \cdots + a_1 y' + a_0 y = 0 \end{equation*}
It is also called the complementary solution. The general solution combines the homogeneous and particular solutions:
\begin{equation*} y = y_h + y_p \end{equation*}
undetermined coefficients.
The initially unknown constants in a guessed particular solution form that are determined by substituting into the differential equation and matching coefficients of like terms. For example, when the forcing function is a quadratic polynomial:
\begin{equation*} y_p = Ax^2 + Bx + C \end{equation*}
modification rule for the particular solution.
If a term in the initial guess for \(y_p\) also appears in the homogeneous solution, multiply any overlapping term in \(y_p\) by the smallest power of \(x\) that eliminates the overlap. For example, if \(e^{3x}\) already appears in \(y_h\text{:}\)
\begin{equation*} Ae^{3x} \to Axe^{3x} \end{equation*}
method of undetermined coefficients.
A method for solving an LNCC equation when the forcing function (forcing term) has a standard polynomial, exponential, or trigonometric form: choose \(y_p\text{,}\) apply the modification rule for the particular solution if needed, substitute, and solve for the undetermined coefficients.
integration by parts.
A calculus method that rewrites an integral of a product using the product rule:
\begin{equation*} \int_a^b u\,dv = uv\Big|_a^b - \int_a^b v\,du \end{equation*}
derivative transfer.
The process of moving a derivative from one function to another inside an integral using integration by parts. In Laplace work, this usually moves the derivative off the unknown function:
\begin{equation*} \int_0^\infty e^{-st}f'(t)\,dt \to s\int_0^\infty e^{-st}f(t)\,dt - f(0) \end{equation*}
Laplace transform.
A transform that converts a function of \(t\) into a function of \(s\text{,}\) defined by
\begin{equation*} \lap{f(t)} = \int_0^\infty e^{-st}f(t)\,dt \end{equation*}
when the integral converges. The transformed function is commonly written \(F(s)=\lap{f(t)}\text{.}\)
improper integral.
An integral with an infinite limit or an unbounded integrand, evaluated as a limit. For example,
\begin{equation*} \int_0^\infty e^{-st}f(t)\,dt = \lim_{b\to\infty}\int_0^b e^{-st}f(t)\,dt \end{equation*}
existence condition for a Laplace transform.
A condition on \(s\) that guarantees the defining improper integral converges to a finite value. In this text’s real-valued setting, for example:
\begin{equation*} \lap{e^{at}}=\frac{1}{s-a}\quad \text{requires } s>a \end{equation*}
transform pair.
A function in the \(t\)-domain together with its Laplace transform in the \(s\)-domain.
\begin{equation*} f(t)=\cos(bt)\quad \longleftrightarrow \quad F(s)=\frac{s}{s^2+b^2} \end{equation*}
linearity property of the Laplace transform.
The Laplace transform preserves sums and constant multiples:
\begin{equation*} \lap{af(t)\pm bg(t)}=a\lap{f(t)}\pm b\lap{g(t)} \end{equation*}
for constants \(a,b\text{.}\)
derivative transfer property of the Laplace transform.
A rule for transforming derivatives that introduces initial conditions. First derivative form:
\begin{equation*} \lap{f'(t)}=sF(s)-f(0),\quad F(s)=\lap{f(t)} \end{equation*}
exponential shifting property.
Multiplying by \(e^{at}\) in the \(t\)-domain shifts \(s\) by \(a\) in the transform:
\begin{equation*} \lap{e^{at}f(t)}=F(s-a) \end{equation*}
where \(F(s)=\lap{f(t)}\text{,}\) for values of \(s\) where the shifted transform converges.
Laplace derivative property.
Multiplication by powers of \(t\) corresponds to derivatives with respect to \(s\text{:}\)
\begin{equation*} \lap{t^n f(t)} = (-1)^n\frac{d^n}{ds^n}\Big[F(s)\Big],\quad n=1,2,3,\ldots \end{equation*}
where \(F(s)=\lap{f(t)}\text{.}\)
\(t\)-domain.
The original function domain before transformation, where functions are written in terms of \(t\text{.}\)
\(s\)-domain.
The transform domain after applying the Laplace transform, where functions are written in terms of \(s\text{.}\)
forward transform.
In the Laplace transform method, the forward transform means applying the Laplace transform to both sides of a differential equation so it becomes an algebraic equation in the \(s\)-domain.
\begin{equation*} \lap{y'' - 9y} = \lap{10e^{2t}} \quad\Rightarrow\quad s^2Y - 9Y - s + 7 = \frac{10}{s-2} \end{equation*}
Laplace-domain equation.
The algebraic equation in \(Y(s)\) obtained after the forward transform of a differential equation.
\begin{equation*} s^2Y - 9Y - s + 7 = \frac{10}{s-2} \end{equation*}
partial fraction decomposition (PFD).
A method for rewriting a rational function as a sum of simpler rational terms, usually so each term can match an inverse Laplace transform form.
\begin{equation*} \frac{2s+5}{(s+1)(s+4)} = \frac{1}{s+1} + \frac{1}{s+4} \end{equation*}
completing the square.
An algebra step used to rewrite a quadratic denominator so it matches a standard inverse Laplace transform form.
\begin{equation*} s^2 - 6s + 14 = (s-3)^2 + 5 \end{equation*}
inverse Laplace transform.
The operation that recovers a function in the \(t\)-domain from its Laplace transform in the \(s\)-domain, undoing the forward transform.
\begin{equation*} \ilap{\frac{1}{s-a}} = e^{at} \end{equation*}
backward transform.
In the Laplace transform method, the backward transform means applying the inverse Laplace transform term-by-term to \(Y(s)\) to return to the original solution \(y(t)\text{.}\)
Laplace transform method.
A three-step method for solving an initial-value problem: use the forward transform, solve and prepare \(Y(s)\) in the \(s\)-domain, then use the backward transform to recover \(y(t)\text{.}\)
unit step function (Heaviside function).
The basic ON-OFF switch that jumps from \(0\) to \(1\) at \(t=0\text{:}\)
\begin{equation*} u(t)=\begin{cases} 1, & t\ge 0\\ 0, & t<0 \end{cases} \end{equation*}
shifted unit step function.
A unit step function (Heaviside function) that turns ON at \(t=c\) instead of at \(t=0\text{:}\)
\begin{equation*} u_c(t)=u(t-c)=\begin{cases} 1, & t\ge c\\ 0, & t<c \end{cases} \end{equation*}
reversed unit step function.
A step switch that is ON before \(t=c\) and OFF afterward:
\begin{equation*} 1-u_c(t)=\begin{cases} 1, & t<c\\ 0, & t\ge c \end{cases} \end{equation*}
windowed unit step function.
A switch that is ON only on a finite interval, built from two shifted unit step functions:
\begin{equation*} u_c(t)-u_d(t)=\begin{cases} 1, & c\le t<d\\ 0, & \text{otherwise} \end{cases} \end{equation*}
piecewise function.
A function defined by different formulas on different parts of its domain. For example,
\begin{equation*} g(t)=\begin{cases} \sin t, & t<0\\ 2e^{-t}, & 0\le t<2\\ 1.5, & t\ge 2 \end{cases} \end{equation*}
unit step form.
A way to rewrite a piecewise function as a single expression using step-function switches, such as
\begin{equation*} g(t)=2t\bigl(u_0(t)-u_1(t)\bigr)+3\bigl(u_1(t)-u_4(t)\bigr) \end{equation*}
piecewise forcing term.
A forcing function (forcing term) whose formula changes across intervals of time. It is typically rewritten in unit step form before applying the Laplace transform.
Laplace step rule for \(u_c(t)\).
The Laplace transform of a shifted unit step function:
\begin{equation*} \lap{u_c(t)}=\frac{e^{-cs}}{s},\qquad s>0 \end{equation*}
Laplace step rule for \(f(t)u_c(t)\).
A rule for transforming a function that turns ON at \(t=c\text{:}\)
\begin{equation*} \lap{f(t)u_c(t)}=e^{-cs}\lap{f(t+c)} \end{equation*}
Laplace step rule for \(f(t-c)u_c(t)\).
A rule for transforming a shifted function that starts at \(t=c\text{:}\)
\begin{equation*} \lap{f(t-c)u_c(t)}=e^{-cs}F(s),\qquad F(s)=\lap{f(t)} \end{equation*}
system of differential equations.
A collection of differential equations that must be solved together because the dependent variables influence one another or are linked in a single model.
\begin{align*} \frac{dx}{dt} \amp = f(x,y)\\ \frac{dy}{dt} \amp = g(x,y) \end{align*}
uncoupled system.
A system of differential equations in which each equation involves only its own dependent variable, so each equation can be solved independently.
\begin{align*} \frac{dx}{dt} \amp = -x\\ \frac{dy}{dt} \amp = -2y \end{align*}
partially coupled system.
A system in which one equation is independent, but another equation depends on more than one dependent variable.
\begin{align*} \frac{dx}{dt} \amp = -x\\ \frac{dy}{dt} \amp = -2y + x \end{align*}
fully coupled system.
A system in which no equation stands alone because each equation depends on multiple dependent variables.
\begin{align*} \frac{dx}{dt} \amp = x + y\\ \frac{dy}{dt} \amp = x - y \end{align*}
linear system.
A system whose dependent variables appear only to the first power and are not multiplied together or placed inside nonlinear functions. For two variables, a constant-coefficient linear system has the form
\begin{align*} \frac{dx}{dt} \amp = ax + by\\ \frac{dy}{dt} \amp = cx + dy \end{align*}
coefficient matrix.
For a linear system, the matrix that collects the coefficients of the dependent variables in the equation \(\frac{dY}{dt}=AY\text{,}\) where \(Y\) is the state vector.
\begin{align*} \frac{dY}{dt} = AY,\qquad A=\begin{bmatrix} a \amp b \\ c \amp d \end{bmatrix} \end{align*}
state vector.
A column vector listing the dependent variables in a system.
\begin{gather*} Y=\begin{bmatrix} x \\ y \end{bmatrix} \end{gather*}
planar system.
A system with two dependent variables. Its state vector has two components, and its solutions can be drawn in the phase plane.
dimension (of a system).
The number of dependent variables in a system, equal to the number of entries in its state vector.
\begin{gather*} Y=\begin{bmatrix} y_1 \\ y_2 \\ y_3 \end{bmatrix}\qquad\text{dimension } 3 \end{gather*}
eigenvalue.
A number \(r\) for which a matrix \(A\) has a nonzero vector \(\vec{v}\) satisfying
\begin{gather*} A\vec{v}=r\vec{v} \end{gather*}
For a linear system, eigenvalues determine exponential growth, decay, and oscillation rates.
eigenvector.
A nonzero vector \(\vec{v}\) that satisfies
\begin{gather*} A\vec{v}=r\vec{v} \end{gather*}
for some eigenvalue \(r\text{.}\)
first-order system.
A system whose highest derivatives are first derivatives. A second-order equation such as \(y''+3y'+2y=0\) can be rewritten as a first-order system by introducing a new variable for the first derivative.
\begin{align*} u=y,\quad v=y'\\ u' \amp = v\\ v' \amp = -3v-2u \end{align*}
Euler update (for a system).
The step used by Euler’s method when approximating a system. If
\begin{gather*} \frac{dY}{dt}=F(t,Y) \end{gather*}
then the next approximation is
\begin{gather*} Y_{k+1}=Y_k+h\,F(t_k,Y_k) \end{gather*}
where \(h\) is the step size.
phase plane.
The coordinate plane whose axes are the dependent variables of a two-dimensional system. A solution appears as a path in the \((x,y)\)-plane rather than as separate graphs versus time.
trajectory.
The curve traced in the phase plane by a solution pair \((x(t),y(t))\) as time changes.
direction field (for a system).
A grid of arrows in the phase plane showing the velocity vector of the system at each point.
\begin{gather*} \text{arrow at }(x,y):\ \left(\frac{dx}{dt},\frac{dy}{dt}\right) \end{gather*}
phase portrait.
A picture of the qualitative behavior of a system in the phase plane, usually showing several trajectories, a direction field (for a system), or both.
stable node.
A sink (stable equilibrium) in the phase plane where nearby trajectories move toward the equilibrium without spiraling.
saddle.
An equilibrium point with attracting directions and repelling directions, so some trajectories move toward it while others move away.
spiral sink.
A sink (stable equilibrium) in the phase plane where nearby trajectories spiral inward as time increases.
spiral source.
A source (unstable equilibrium) in the phase plane where nearby trajectories spiral outward as time increases.