kailaix

using PyPlot
using ADCMEKit

NT = 2000
n = 100
Δt = 1.0/NT 
Δx = 1/n 

method = "Central"

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std::string survey_fname;
std::string dir(__FILE__);
dir = dir.substr(0, dir.find_last_of("\\/"));

kailaix

for n in 50 60 70 80 90 100
do
    julia euro_main.jl $n 2>&1 | tee -a euro$n.log &
done

wait %1 %2 %3 %4 %5 %6

for n in 50 70 90 110 130 150
do
    julia flap_main.jl $n 2>&1 | tee -a flap$n.log &

kailaix

using LinearAlgebra
using PyPlot
using SparseArrays
# https://uu.diva-portal.org/smash/get/diva2:1148496/FULLTEXT01.pdf page 8
# https://regulation.pstat.ucsb.edu/sites/sa-wcmf6/5-Peng_ECIR_Model_Qidi.pdf page 13

# params
r0 = 0.5
L = 4.0
a = 1.0

kailaix

# modified version from http://edwardlib.org/tutorials/
from tensorflow_probability import edward2 as ed
import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np

def logistic_regression(features):
  coeffs = ed.Normal(loc=0., scale=1.,
                     sample_shape=features.shape[1], name="coeffs")
  outcomes = ed.Bernoulli(logits=tf.tensordot(features, coeffs, [[1], [0]]),

kailaix

kailaix

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
"""

kailaix

# 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

kailaix

# 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

kailaix

# 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