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You would need to linearize the problem. I prefer to do it before discretization but it's possible to do also after discretization. (I'm a bit skeptical of linearization after discretization because I have never looked into the details. In general, discretization and linearization steps do not commute.)

In the following I assume that the equation is actually $\partial_t u = \partial_x(a(u) \partial_x u)$ and that you have the boundary condition $u=0$.

The weak form is $$(\partial_t u, v) = -(a(u) \partial_x u, \partial_x v).$$ I prefer to first do the time discretization so that you see the structure of the resulting problem. E.g., implicit Euler method leads to $$(\delta^{-1}(u_n - u_{n-1}), v) = -(a(u_n) \partial_x u_n, \partial_x v),$$ or, equivalently, $$(\delta^{-1} u_n, v) + (a(u_n) \partial_x u_n, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $n$ runs over the time steps and $\delta > 0$ is the size of the step. The equation is still nonlinear in $u_n$ and you must linearize. One option is to do a fixed-point iteration (inside each time step $n$) by repeatedly finding $u_{k,n}$ from $$(\delta^{-1} u_{k,n}, v) + (a(u_{k-1,n}) \partial_x u_{k,n}, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $k$ runs over the linearization steps and $u_{k-1,n}$ is the function from the previous iteration. Notice how you now have two iterations: one for time discretization and one for linearization.

I made an example case with $u(x) = \sin(\pi x)$ and solved it using the code I know the best (i.e. my own, you can install it in Python using pip install scikit-fem==2.0.0 if you want to run it):

from skfem import *
from skfem.helpers import *
from skfem.visuals.matplotlib import *
import numpy as np

m = MeshLine(); m.refine(5)
basis = InteriorBasis(m, ElementLineP1())
a = lambda w: (1. * w) ** 2
bilinf_stiffness = BilinearForm(lambda u, v, w: a(w['u_prev']) * dot(grad(u), grad(v)))
delta = 0.1
M = BilinearForm(lambda u, v, w: 1. / delta * u * v).assemble(basis)
load = LinearForm(lambda v, w: 1. / delta * w['u_prev'] * v)

u = np.sin(np.pi * m.p[0])
plot(m, u)
for n in range(10): # 10 time steps 
    for k in range(250): # 250 linearization loops 
        A = bilinf_stiffness.assemble(basis, u_prev=basis.interpolate(u))
        b = load.assemble(basis, u_prev=basis.interpolate(u))
        u = solve(*condense(A + M, b, D=m.boundary_nodes()))
plot(m, u); show()

This gives the following two pictures (initial condition and the result at $t=1$):

There are obviously lots of alternative ways of doing this, but this should give you the general idea.

You would need to linearize the problem. I prefer to do it before discretization but it's possible to do also after discretization. (I'm a bit skeptical of linearization after discretization because I have never looked into the details. In general, discretization and linearization steps do not commute.)

In the following I assume that the equation is actually $\partial_t u = \partial_x(a(u) \partial_x u)$ and that you have the boundary condition $u=0$.

The weak form is $$(\partial_t u, v) = -(a(u) \partial_x u, \partial_x v).$$ I prefer to first do the time discretization so that you see the structure of the resulting problem. E.g., implicit Euler method leads to $$(\delta^{-1}(u_n - u_{n-1}), v) = -(a(u_n) \partial_x u_n, \partial_x v),$$ or, equivalently, $$(\delta^{-1} u_n, v) + (a(u_n) \partial_x u_n, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $n$ runs over the time steps and $\delta > 0$ is the size of the step. The equation is still nonlinear in $u_n$ and you must linearize. One option is to do a fixed-point iteration (inside each time step $n$) by repeatedly finding $u_{k,n}$ from $$(\delta^{-1} u_{k,n}, v) + (a(u_{k-1,n}) \partial_x u_{k,n}, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $k$ runs over the linearization steps and $u_{k-1,n}$ is the function from the previous iteration. Notice how you now have two iterations: one for time discretization and one for linearization.

I made an example case with $u(x) = \sin(\pi x)$ and solved it using the code I know the best (i.e. my own, you can install it in Python using pip install scikit-fem==2.0.0 if you want to run it):

from skfem import *
from skfem.helpers import *
from skfem.visuals.matplotlib import *
import numpy as np

m = MeshLine(); m.refine(5)
basis = InteriorBasis(m, ElementLineP2())
a = lambda w: (1. * w) ** 2
bilinf_stiffness = BilinearForm(lambda u, v, w: a(w['u_prev']) * dot(grad(u), grad(v)))
delta = 0.01
M = BilinearForm(lambda u, v, w: 1. / delta * u * v).assemble(basis)
load = LinearForm(lambda v, w: 1. / delta * w['u_prev'] * v)

u = project(lambda x: np.sin(np.pi * x[0]), basis_to=basis)
plot(basis, u)
for n in range(100): # 100 time steps 
    b = load.assemble(basis, u_prev=basis.interpolate(u))
    for k in range(250): # 250 linearization loops
        A = bilinf_stiffness.assemble(basis, u_prev=basis.interpolate(u))
        u = solve(*condense(A + M, b, D=m.boundary_nodes()))
    print("iteration {}".format(n))
plot(basis, u) 
show()

This gives the following two pictures (initial condition and the result at $t=1$):

There are obviously lots of alternative ways of doing this, but this should give you the general idea.

You would need to linearize the problem. I prefer to do it before discretization but it's possible to do also after discretization. (I'm a bit skeptical of linearization after discretization because I have never looked into the details. In general, discretization and linearization steps do not commute.)

In the following I assume that the equation is actually $\partial_t u = \partial_x(a(u) \partial_x u)$ and that you have the boundary condition $u=0$.

The weak form is $$(\partial_t u, v) = -(a(u) \partial_x u, \partial_x v).$$ I prefer to first do the time discretization so that you see the structure of the resulting problem. E.g., implicit Euler method leads to $$(\delta^{-1}(u_n - u_{n-1}), v) = -(a(u_n) \partial_x u_n, \partial_x v),$$ or, equivalently, $$(\delta^{-1} u_n, v) + (a(u_n) \partial_x u_n, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $n$ runs over the time steps and $\delta > 0$ is the size of the step. The equation is still nonlinear in $u_n$ and you must linearize. One option is to do a fixed-point iteration (inside each time step $n$) by repeatedly finding $u_{k,n}$ from $$(\delta^{-1} u_{k,n}, v) + (a(u_{k-1,n}) \partial_x u_{k,n}, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $k$ runs over the linearization steps and $u_{k-1,n}$ is the function from the previous iteration. Notice how you now have two iterations: one for time discretization and one for linearization.

I made an example case with $u(x) = \sin(\pi x)$ and solved it using the code I know the best (i.e. my own, you can install it in Python using pip install scikit-fem==2.0.0 if you want to run it):

from skfem import *
from skfem.helpers import *
from skfem.visuals.matplotlib import *
import numpy as np

m = MeshLine(); m.refine(5)
basis = InteriorBasis(m, ElementLineP1())
a = lambda w: (1. * w) ** 2
bilinf_stiffness = BilinearForm(lambda u, v, w: a(w['u_prev']) * dot(grad(u), grad(v)))
delta = 0.1
M = BilinearForm(lambda u, v, w: 1. / delta * u * v).assemble(basis)
load = LinearForm(lambda v, w: 1. / delta * w['u_prev'] * v)

u = np.sin(np.pi * m.p[0])
plot(m, u)
for n in range(10): # 10 time steps 
    for k in range(8): # 8 linearization loops 
        A = bilinf_stiffness.assemble(basis, u_prev=basis.interpolate(u))
        b = load.assemble(basis, u_prev=basis.interpolate(u))
        u = solve(*condense(A + M, b, D=m.boundary_nodes()))
plot(m, u); show()

This gives the following two pictures (initial condition and the result at $t=1$):

There are obviously lots of alternative ways of doing this, but this should give you the general idea.

You would need to linearize the problem. I prefer to do it before discretization but it's possible to do also after discretization. (I'm a bit skeptical of linearization after discretization because I have never looked into the details. In general, discretization and linearization steps do not commute.)

In the following I assume that the equation is actually $\partial_t u = \partial_x(a(u) \partial_x u)$ and that you have the boundary condition $u=0$.

The weak form is $$(\partial_t u, v) = -(a(u) \partial_x u, \partial_x v).$$ I prefer to first do the time discretization so that you see the structure of the resulting problem. E.g., implicit Euler method leads to $$(\delta^{-1}(u_n - u_{n-1}), v) = -(a(u_n) \partial_x u_n, \partial_x v),$$ or, equivalently, $$(\delta^{-1} u_n, v) + (a(u_n) \partial_x u_n, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $n$ runs over the time steps and $\delta > 0$ is the size of the step. The equation is still nonlinear in $u_n$ and you must linearize. One option is to do a fixed-point iteration (inside each time step $n$) by repeatedly finding $u_{k,n}$ from $$(\delta^{-1} u_{k,n}, v) + (a(u_{k-1,n}) \partial_x u_{k,n}, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $k$ runs over the linearization steps and $u_{k-1,n}$ is the function from the previous iteration. Notice how you now have two iterations: one for time discretization and one for linearization.

I made an example case with $u(x) = \sin(\pi x)$ and solved it using the code I know the best (i.e. my own, you can install it in Python using pip install scikit-fem==2.0.0 if you want to run it):

from skfem import *
from skfem.helpers import *
from skfem.visuals.matplotlib import *
import numpy as np

m = MeshLine(); m.refine(5)
basis = InteriorBasis(m, ElementLineP1())
a = lambda w: (1. * w) ** 2
bilinf_stiffness = BilinearForm(lambda u, v, w: a(w['u_prev']) * dot(grad(u), grad(v)))
delta = 0.1
M = BilinearForm(lambda u, v, w: 1. / delta * u * v).assemble(basis)
load = LinearForm(lambda v, w: 1. / delta * w['u_prev'] * v)

u = np.sin(np.pi * m.p[0])
plot(m, u)
for n in range(10): # 10 time steps 
    for k in range(250): # 250 linearization loops 
        A = bilinf_stiffness.assemble(basis, u_prev=basis.interpolate(u))
        b = load.assemble(basis, u_prev=basis.interpolate(u))
        u = solve(*condense(A + M, b, D=m.boundary_nodes()))
plot(m, u); show()

This gives the following two pictures (initial condition and the result at $t=1$):

There are obviously lots of alternative ways of doing this, but this should give you the general idea.

You would need to linearize the problem. I prefer to do it before discretization but it's possible to do also after discretization. (I'm a bit skeptical of linearization after discretization because I have never looked into the details. In general, discretization and linearization steps do not commute.)

In the following I assume that the equation is actually $\partial_t u = \partial_x(a(u) \partial_x u)$ and that you have the boundary condition $u=0$.

The weak form is $$(\partial_t u, v) = -(a(u) \partial_x u, \partial_x v).$$ I prefer to first do the time discretization so that you see the structure of the resulting problem. E.g., implicit Euler method leads to $$(\delta^{-1}(u_n - u_{n-1}), v) = -(a(u_n) \partial_x u_n, \partial_x v),$$ or, equivalently, $$(\delta^{-1} u_n, v) + (a(u_n) \partial_x u_n, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $n$ runs over the time steps and $\delta > 0$ is the size of the step. The equation is still nonlinear in $u_n$ and you must linearize. One option is to do a fixed-point iteration (inside each time step $n$) by repeatedly finding $u_{k,n}$ from $$(\delta^{-1} u_{k,n}, v) + (a(u_{k-1,n}) \partial_x u_{k,n}, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $k$ runs over the linearization steps and $u_{k-1,n}$ is the function from the previous iteration. Notice how you now have two iterations: one for time discretization and one for linearization.

I made an example case with $u(x) = \sin(\pi x)$ and solved it using the code I know the best (i.e. my own):

from skfem import *
from skfem.helpers import *
from skfem.visuals.matplotlib import *
import numpy as np

m = MeshLine(); m.refine(5)
basis = InteriorBasis(m, ElementLineP1())
a = lambda w: (1. * w) ** 2
bilinf_stiffness = BilinearForm(lambda u, v, w: a(w['u_prev']) * dot(grad(u), grad(v)))
delta = 0.1
M = BilinearForm(lambda u, v, w: 1. / delta * u * v).assemble(basis)
load = LinearForm(lambda v, w: 1. / delta * w['u_prev'] * v)

u = np.sin(np.pi * m.p[0])
plot(m, u)
for n in range(10): # 10 time steps 
    for k in range(8): # 8 linearization loops 
        A = bilinf_stiffness.assemble(basis, u_prev=basis.interpolate(u))
        b = load.assemble(basis, u_prev=basis.interpolate(u))
        u = solve(*condense(A + M, b, D=m.boundary_nodes()))
plot(m, u); show()

This gives the following two pictures (initial condition and the result at $t=1$):

There are obviously lots of alternative ways of doing this, but this should get you started.

You would need to linearize the problem. I prefer to do it before discretization but it's possible to do also after discretization. (I'm a bit skeptical of linearization after discretization because I have never looked into the details. In general, discretization and linearization steps do not commute.)

In the following I assume that the equation is actually $\partial_t u = \partial_x(a(u) \partial_x u)$ and that you have the boundary condition $u=0$.

The weak form is $$(\partial_t u, v) = -(a(u) \partial_x u, \partial_x v).$$ I prefer to first do the time discretization so that you see the structure of the resulting problem. E.g., implicit Euler method leads to $$(\delta^{-1}(u_n - u_{n-1}), v) = -(a(u_n) \partial_x u_n, \partial_x v),$$ or, equivalently, $$(\delta^{-1} u_n, v) + (a(u_n) \partial_x u_n, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $n$ runs over the time steps and $\delta > 0$ is the size of the step. The equation is still nonlinear in $u_n$ and you must linearize. One option is to do a fixed-point iteration (inside each time step $n$) by repeatedly finding $u_{k,n}$ from $$(\delta^{-1} u_{k,n}, v) + (a(u_{k-1,n}) \partial_x u_{k,n}, \partial_x v) = (\delta^{-1}u_{n-1}, v),$$ where $k$ runs over the linearization steps and $u_{k-1,n}$ is the function from the previous iteration. Notice how you now have two iterations: one for time discretization and one for linearization.

I made an example case with $u(x) = \sin(\pi x)$ and solved it using the code I know the best (i.e. my own, you can install it in Python using pip install scikit-fem==2.0.0 if you want to run it):

from skfem import *
from skfem.helpers import *
from skfem.visuals.matplotlib import *
import numpy as np

m = MeshLine(); m.refine(5)
basis = InteriorBasis(m, ElementLineP1())
a = lambda w: (1. * w) ** 2
bilinf_stiffness = BilinearForm(lambda u, v, w: a(w['u_prev']) * dot(grad(u), grad(v)))
delta = 0.1
M = BilinearForm(lambda u, v, w: 1. / delta * u * v).assemble(basis)
load = LinearForm(lambda v, w: 1. / delta * w['u_prev'] * v)

u = np.sin(np.pi * m.p[0])
plot(m, u)
for n in range(10): # 10 time steps 
    for k in range(8): # 8 linearization loops 
        A = bilinf_stiffness.assemble(basis, u_prev=basis.interpolate(u))
        b = load.assemble(basis, u_prev=basis.interpolate(u))
        u = solve(*condense(A + M, b, D=m.boundary_nodes()))
plot(m, u); show()

This gives the following two pictures (initial condition and the result at $t=1$):

There are obviously lots of alternative ways of doing this, but this should give you the general idea.