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def find_largest(L):
    N = len(L)
    minL = [ [None] * N for j in range(N)]
    sumL = [ [None] * N for j in range(N)]

    def helper(i):
        if i==0:
            minL[0][0] = L[0]
        else:
            for j in range(i):

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using PyPlot
using PyCall
@pyimport matplotlib2tikz as mpl
mpl.save("figures/fig.tex")

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# This is an example of solving the Poisson problem using the Assembler Programming method
#           (-Δ) u(x) = f(x)
#           u(x) = 0, x ∈ [0,1]^2
#  The exact solution is u(x,y) = sin(pi*x)sin(pi*y)
#  and f(x,y) = 2pi^2 * sin(pi*x)sin(pi*y)

using PyPlot
import Plots
using PyCall
using SparseArrays

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# This example solves the plane stress problem
# E/(2(1+ν)) ∇^2 u_1 + E/(2(1-ν)) * ∂/∂x ( ∂u_1/∂x + ∂u_2/∂y )  = f1
# E/(2(1+ν)) ∇^2 u_2 + E/(2(1-ν)) * ∂/∂y ( ∂u_1/∂x + ∂u_2/∂y )  = f2

# Exact solution = sin(πx)sin(πy)
# f1, f2, σ can be evaluated analytically

using LinearAlgebra
using PyPlot
import Plots

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# solve 1D wave equation with ABC
using PyPlot
using Plots
using LinearAlgebra


N = 100
x = LinRange(-1.,1.,N+1)
Δx = x[2]-x[1]
NT = 200

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using TensorFlow

num_layers = 3 # number of hidden layer
nh = 20 # number of neurons per hidden layer
"""
neural(x, scope="default", reuse=false)

A Multilayer Perceptron with `num_layers` hidden layers. The last layer is a linear layer. 
The neural network is enough to perform many useful approximations
"""

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