i have been struggling to find out what is the equivalent sympy method in Julia. actually i made finite element method solver (numerical method to solve partial differential equation, parabolic, elliptical and hyperbolic in 2D) and it is working fine in python. but, i am facing the speed issue, as t…

Just two general remarks: if you post code snippets, please enclose them in triple backticks (```) for better readability there is not only numeric and symbolic differentiation, but also automated differentiation which is more common in Julia code. Is that something you could use? You could also …

[image] ufechner7: Is that something you could use? thanks for your response, the finite element method is used for arbitrary domain and not for regular domain. i can not use simple integration and derivative technique as used for regular domain. my problem is intermediate step only, that is,…

I want derivatives like f(“x^2y", x)=2xy and f("x^2y”, y)=x^2 output is string or symbol (partial derivative wrt x and y), any function in julia like this?

SymPy works with symbolic expressions, not strings. You maybe can use sympify, as in diff(sympify("x^3*y"), x), though that will be an issue if you have assumptions on x: julia> @syms x y (x, y) julia> diff(sympify("x^3*y"), x) 2 3⋅x ⋅y julia> @syms x::real (x,) julia> diff(sympify("x^3*y")…

[image] Madan: make string shape functions for each rectangular element in array Don’t do this. Represent functions using functions (including anonymous functions), not strings. Functions are fast, composable, flexible (exposing the full Julia language), and differentiable using any of the m…

[image] stevengj: Don’t do this. Represent functions using functions (including anonymous functions), not strings. thanks, i welcome your suggestion but, if i had not made strings shape function then my solver would have particular for single differential equation and particular boundary and…

[image] j_verzani: SymPy works with symbolic expressions, not strings. You maybe can use sympify, as in diff(sympify("x^3*y"), x), though that will be an issue if you have assumptions on x: thanks, i will try it. will it have speed of Julia? or is it just pycall?

You can have function as inputs of another function. Much more efficient in Julia than carrying a string around. So if the argument of your function is "3*x^2", you could just pass x-> 3*x^2 With respect to differentiation, with automatic differentiation you get an exact evaluation of the derivati…

[image] Veenty: You can have function as inputs of another function. Much more efficient in Julia than carrying a string around. So if the argument of your function is "3*x^2", you could just pass x-> 3*x^2 and what could happens if user changed the functional like "3x^2(yt^2)(other gradient…

for example, solver.setLoad('0') # solver.setRobinCo({1:'1.5'}) # it is dictionary {elementNumber,coefficient value} # solver.setStiffCo({3:'0.4'}) # solver.setMassCo({2:'2.55'}) startTime=time.time() totalElements,npe,cords,lg,dirich,neum, robin=mesh.data('eigen.txt') solver.setFunctional(f"delfx(…

What is sympy (python) equivalent differentiation method in Julia?

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josuagrw November 21, 2022, 12:59pm 18

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What is sympy (python) equivalent differentiation method in Julia?

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