differential equations - PDEs : automatic method choice : TensorProductGrid or FiniteElement?

A problem that arises when we solve PDEs with NDSolve[] and Method->Automatic is how to know which method has been chosen : TensorProductGrid or FiniteElement ?

The question is important because :

NDSolve often use TensorProductGrid (wrongly I think) instead of FiniteElement. I guess the reason is historical : TensorProductGrid is maybe 15 years older than FiniteElement. For compatibility reasons all forms of syntax that was leading to TensorProductGrid should continue to lead to this method.

The wrong choice may lead to problems that are very, very advanced (here for example)

If we don't know which method is used, we don't know which documentation to read (the one for TensorProductGrid or the one for FiniteElement) and these documentations are very consequent (and it's crucial to understand error messages).

Even advanced user can't deduce the choice of the method from the syntax. For example, the use of the expression NeumannValue impose FiniteElement, but Laplacian not, though historically they appeared at the same time.

Here are some ways to find the answer to this question :

some error messages may give the information

after having done sol=NDSolveValue[...], then if sol["ElementMesh"] gives a object ElementMesh[ blabla ], FiniteElement has been used

one can do also NDSolve`ProcessEquations[...] instead of NDSolve[...] and examine the resulting object.

Is there something more friendly, above all for beginners who will use preferently the automatic choice ?

Comments

plotting - Plot 4D data with color as 4th dimension

I have a list of 4D data (x position, y position, amplitude, wavelength). I want to plot x, y, and amplitude on a 3D plot and have the color of the points correspond to the wavelength. I have seen many examples using functions to define color but my wavelength cannot be expressed by an analytic function. Is there a simple way to do this? Answer Here a another possible way to visualize 4D data: data = Flatten[Table[{x, y, x^2 + y^2, Sin[x - y]}, {x, -Pi, Pi,Pi/10}, {y,-Pi,Pi, Pi/10}], 1]; You can use the function Point along with VertexColors . Now the points are places using the first three elements and the color is determined by the fourth. In this case I used Hue, but you can use whatever you prefer. Graphics3D[ Point[data[[All, 1 ;; 3]], VertexColors -> Hue /@ data[[All, 4]]], Axes -> True, BoxRatios -> {1, 1, 1/GoldenRatio}]

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plotting - Mathematica: 3D plot based on combined 2D graphs

I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1....

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