Solving an ODE containing a function of the independent variable only known at discrete points
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I'm a little new at Mathematica and I'm trying to solve the following ordinary differential equation:
$qquad xfracdy(x)dx+y=-p(x)$.
I have data (in a list) for $x$, and the corresponding values of $p(x)$ (also in a list). I need to solve the ODE to get a list $y(x)$ for the $x$s in the 1st list. I've been trying to use NDSolve
, like so:
NDSolve[x*y'[x] + y[x] == -p, y, x, a, b]
with no luck.
Is this possible in Mathematica? Is there a way to do this?
list-manipulation differential-equations equation-solving ode
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up vote
2
down vote
favorite
I'm a little new at Mathematica and I'm trying to solve the following ordinary differential equation:
$qquad xfracdy(x)dx+y=-p(x)$.
I have data (in a list) for $x$, and the corresponding values of $p(x)$ (also in a list). I need to solve the ODE to get a list $y(x)$ for the $x$s in the 1st list. I've been trying to use NDSolve
, like so:
NDSolve[x*y'[x] + y[x] == -p, y, x, a, b]
with no luck.
Is this possible in Mathematica? Is there a way to do this?
list-manipulation differential-equations equation-solving ode
In other words are you trying to fit the differential equation to your data ?
â Lotus
Sep 5 at 3:33
@Lotus yes I guess so!
â zack
Sep 5 at 3:40
Then here is what you do: Create a cost function with your differential equation (NDSolve etc..) and the Norm between the solution and your data. Use NMinimize to minimize the cost function to get y(x) which should be a good fit
â Lotus
Sep 5 at 3:42
@Lotus This is a misunderstanding. If I am not mistaken, OP has just some discrete data instead a function for the excitationsp
(the right hand side) and wants to simply solve the ODE fory
.
â Henrik Schumacher
Sep 5 at 6:20
add a comment |Â
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I'm a little new at Mathematica and I'm trying to solve the following ordinary differential equation:
$qquad xfracdy(x)dx+y=-p(x)$.
I have data (in a list) for $x$, and the corresponding values of $p(x)$ (also in a list). I need to solve the ODE to get a list $y(x)$ for the $x$s in the 1st list. I've been trying to use NDSolve
, like so:
NDSolve[x*y'[x] + y[x] == -p, y, x, a, b]
with no luck.
Is this possible in Mathematica? Is there a way to do this?
list-manipulation differential-equations equation-solving ode
I'm a little new at Mathematica and I'm trying to solve the following ordinary differential equation:
$qquad xfracdy(x)dx+y=-p(x)$.
I have data (in a list) for $x$, and the corresponding values of $p(x)$ (also in a list). I need to solve the ODE to get a list $y(x)$ for the $x$s in the 1st list. I've been trying to use NDSolve
, like so:
NDSolve[x*y'[x] + y[x] == -p, y, x, a, b]
with no luck.
Is this possible in Mathematica? Is there a way to do this?
list-manipulation differential-equations equation-solving ode
list-manipulation differential-equations equation-solving ode
edited Sep 5 at 5:36
m_goldberg
82k869190
82k869190
asked Sep 5 at 3:10
zack
183
183
In other words are you trying to fit the differential equation to your data ?
â Lotus
Sep 5 at 3:33
@Lotus yes I guess so!
â zack
Sep 5 at 3:40
Then here is what you do: Create a cost function with your differential equation (NDSolve etc..) and the Norm between the solution and your data. Use NMinimize to minimize the cost function to get y(x) which should be a good fit
â Lotus
Sep 5 at 3:42
@Lotus This is a misunderstanding. If I am not mistaken, OP has just some discrete data instead a function for the excitationsp
(the right hand side) and wants to simply solve the ODE fory
.
â Henrik Schumacher
Sep 5 at 6:20
add a comment |Â
In other words are you trying to fit the differential equation to your data ?
â Lotus
Sep 5 at 3:33
@Lotus yes I guess so!
â zack
Sep 5 at 3:40
Then here is what you do: Create a cost function with your differential equation (NDSolve etc..) and the Norm between the solution and your data. Use NMinimize to minimize the cost function to get y(x) which should be a good fit
â Lotus
Sep 5 at 3:42
@Lotus This is a misunderstanding. If I am not mistaken, OP has just some discrete data instead a function for the excitationsp
(the right hand side) and wants to simply solve the ODE fory
.
â Henrik Schumacher
Sep 5 at 6:20
In other words are you trying to fit the differential equation to your data ?
â Lotus
Sep 5 at 3:33
In other words are you trying to fit the differential equation to your data ?
â Lotus
Sep 5 at 3:33
@Lotus yes I guess so!
â zack
Sep 5 at 3:40
@Lotus yes I guess so!
â zack
Sep 5 at 3:40
Then here is what you do: Create a cost function with your differential equation (NDSolve etc..) and the Norm between the solution and your data. Use NMinimize to minimize the cost function to get y(x) which should be a good fit
â Lotus
Sep 5 at 3:42
Then here is what you do: Create a cost function with your differential equation (NDSolve etc..) and the Norm between the solution and your data. Use NMinimize to minimize the cost function to get y(x) which should be a good fit
â Lotus
Sep 5 at 3:42
@Lotus This is a misunderstanding. If I am not mistaken, OP has just some discrete data instead a function for the excitations
p
(the right hand side) and wants to simply solve the ODE for y
.â Henrik Schumacher
Sep 5 at 6:20
@Lotus This is a misunderstanding. If I am not mistaken, OP has just some discrete data instead a function for the excitations
p
(the right hand side) and wants to simply solve the ODE for y
.â Henrik Schumacher
Sep 5 at 6:20
add a comment |Â
2 Answers
2
active
oldest
votes
up vote
5
down vote
Why not try to fit p(x)
to the data and then using DSolve
? Since you did not make a MWE, I made up some data
ClearAll[x, y, p]
xData = 1, 2, 3, 4;
pData = 1, 4, 9, 16;
data = Transpose[xData, pData];
p[x_] = Fit[data, 1, x, x^2, x]; (*change fit as needed*)
DSolve[x y'[x] + y[x] == -p[x], y[x], x]
2
Should the data be free of noise, usingInterpolation
instead ofFit
also comes to mind.
â Henrik Schumacher
Sep 5 at 5:30
add a comment |Â
up vote
2
down vote
It is a very simple equation and I recommend to first solve it analytically. It can be done by the, say, the so-called, u*v method. The solution is as follows:
Now one can directly integrate it numerically by summing up the areas of the trapezoids formed by the points x1,x2,p1 and p2. Alternatively, one can use the advice of @Nasser and integrate the fitted function, which is easier.
Have fun!
add a comment |Â
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
5
down vote
Why not try to fit p(x)
to the data and then using DSolve
? Since you did not make a MWE, I made up some data
ClearAll[x, y, p]
xData = 1, 2, 3, 4;
pData = 1, 4, 9, 16;
data = Transpose[xData, pData];
p[x_] = Fit[data, 1, x, x^2, x]; (*change fit as needed*)
DSolve[x y'[x] + y[x] == -p[x], y[x], x]
2
Should the data be free of noise, usingInterpolation
instead ofFit
also comes to mind.
â Henrik Schumacher
Sep 5 at 5:30
add a comment |Â
up vote
5
down vote
Why not try to fit p(x)
to the data and then using DSolve
? Since you did not make a MWE, I made up some data
ClearAll[x, y, p]
xData = 1, 2, 3, 4;
pData = 1, 4, 9, 16;
data = Transpose[xData, pData];
p[x_] = Fit[data, 1, x, x^2, x]; (*change fit as needed*)
DSolve[x y'[x] + y[x] == -p[x], y[x], x]
2
Should the data be free of noise, usingInterpolation
instead ofFit
also comes to mind.
â Henrik Schumacher
Sep 5 at 5:30
add a comment |Â
up vote
5
down vote
up vote
5
down vote
Why not try to fit p(x)
to the data and then using DSolve
? Since you did not make a MWE, I made up some data
ClearAll[x, y, p]
xData = 1, 2, 3, 4;
pData = 1, 4, 9, 16;
data = Transpose[xData, pData];
p[x_] = Fit[data, 1, x, x^2, x]; (*change fit as needed*)
DSolve[x y'[x] + y[x] == -p[x], y[x], x]
Why not try to fit p(x)
to the data and then using DSolve
? Since you did not make a MWE, I made up some data
ClearAll[x, y, p]
xData = 1, 2, 3, 4;
pData = 1, 4, 9, 16;
data = Transpose[xData, pData];
p[x_] = Fit[data, 1, x, x^2, x]; (*change fit as needed*)
DSolve[x y'[x] + y[x] == -p[x], y[x], x]
answered Sep 5 at 3:46
Nasser
56.7k485203
56.7k485203
2
Should the data be free of noise, usingInterpolation
instead ofFit
also comes to mind.
â Henrik Schumacher
Sep 5 at 5:30
add a comment |Â
2
Should the data be free of noise, usingInterpolation
instead ofFit
also comes to mind.
â Henrik Schumacher
Sep 5 at 5:30
2
2
Should the data be free of noise, using
Interpolation
instead of Fit
also comes to mind.â Henrik Schumacher
Sep 5 at 5:30
Should the data be free of noise, using
Interpolation
instead of Fit
also comes to mind.â Henrik Schumacher
Sep 5 at 5:30
add a comment |Â
up vote
2
down vote
It is a very simple equation and I recommend to first solve it analytically. It can be done by the, say, the so-called, u*v method. The solution is as follows:
Now one can directly integrate it numerically by summing up the areas of the trapezoids formed by the points x1,x2,p1 and p2. Alternatively, one can use the advice of @Nasser and integrate the fitted function, which is easier.
Have fun!
add a comment |Â
up vote
2
down vote
It is a very simple equation and I recommend to first solve it analytically. It can be done by the, say, the so-called, u*v method. The solution is as follows:
Now one can directly integrate it numerically by summing up the areas of the trapezoids formed by the points x1,x2,p1 and p2. Alternatively, one can use the advice of @Nasser and integrate the fitted function, which is easier.
Have fun!
add a comment |Â
up vote
2
down vote
up vote
2
down vote
It is a very simple equation and I recommend to first solve it analytically. It can be done by the, say, the so-called, u*v method. The solution is as follows:
Now one can directly integrate it numerically by summing up the areas of the trapezoids formed by the points x1,x2,p1 and p2. Alternatively, one can use the advice of @Nasser and integrate the fitted function, which is easier.
Have fun!
It is a very simple equation and I recommend to first solve it analytically. It can be done by the, say, the so-called, u*v method. The solution is as follows:
Now one can directly integrate it numerically by summing up the areas of the trapezoids formed by the points x1,x2,p1 and p2. Alternatively, one can use the advice of @Nasser and integrate the fitted function, which is easier.
Have fun!
answered Sep 5 at 7:36
Alexei Boulbitch
20.4k2369
20.4k2369
add a comment |Â
add a comment |Â
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In other words are you trying to fit the differential equation to your data ?
â Lotus
Sep 5 at 3:33
@Lotus yes I guess so!
â zack
Sep 5 at 3:40
Then here is what you do: Create a cost function with your differential equation (NDSolve etc..) and the Norm between the solution and your data. Use NMinimize to minimize the cost function to get y(x) which should be a good fit
â Lotus
Sep 5 at 3:42
@Lotus This is a misunderstanding. If I am not mistaken, OP has just some discrete data instead a function for the excitations
p
(the right hand side) and wants to simply solve the ODE fory
.â Henrik Schumacher
Sep 5 at 6:20