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Train_mean.py
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from scipy.integrate import solve_ivp
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import jax
import jax.numpy as jnp
from jax import grad, jit, lax
from jax.experimental.ode import odeint
from jax.example_libraries.optimizers import adam
plt.rcParams['font.size'] = 20
plt.rcParams['lines.linewidth'] = 2
plt.rcParams['axes.linewidth'] = 2
def prbs(cf):
cf = cf + np.random.normal(0,5)
return cf
def Reactor(t,x,*args):
Qf, V, cf, k1, k2, k_2 = args
ca, cb, cc = x
dcadt = Qf*(cf - ca)/V - k1*ca
dcbdt = -Qf*cb/V + k1*ca - 3*(k2*cb**2 - k_2*cc)
dccdt = -Qf*cc/V + k2*cb**2 - k_2*cc
return dcadt, dcbdt, dccdt
t_start = 0
t_end = 200
num_step_ups = 5
t_jumps = t_end/num_step_ups
A,B,C, ta, cfa = [],[],[],[],[]
initial_conditions = [0.3, 0.2, 0.1]
np.random.seed(100)
parameters = [[0.6, 15, 0.3, 0.2, 0.5, 0.1]]
parameters = [list(inner_list) for inner_list in parameters for _ in range(5)]
for i in range(len(parameters)-1):
parameters[i+1][2] = np.random.uniform(0,1.5)
for i, params in enumerate(parameters):
t_start = t_jumps * i
t_end = t_jumps * (i + 1)
t = np.linspace(t_start, t_end, 100)
sol = solve_ivp(Reactor, [t_start, t_end], initial_conditions, method="RK45", t_eval=t, args=params)
initial_conditions = sol.y[:, -1]
A.append(sol.y[0])
B.append(sol.y[1])
C.append(sol.y[2])
ta.append(t)
A = np.concatenate(A)
B = np.concatenate(B)
C = np.concatenate(C)
A = A + np.random.normal(0,0.01,len(A))
B = B + np.random.normal(0,0.01,len(B))
C = C + np.random.normal(0,0.01,len(C))
t = np.concatenate(ta)
for i in range(len(parameters)):
cfa.append([parameters[i][2]]*100)
cfa = np.concatenate(cfa)
def neural_net(params, x, kp):
xz = x
Qf, V, cf = kp
for W, b in params:
xz = jnp.tanh(jnp.dot(W, xz) + b)
dcadt = Qf*(cf - x[0])/V - xz[0]
dcbdt = -Qf*(x[1])/V + xz[0] - xz[1]
return jnp.array([dcadt, dcbdt])
def ode_nn(params, x, cf, kp, z):
xnew = jnp.concatenate([x, jnp.array([cf]), z])
return neural_net(params, xnew, kp)
def update_z(z, new_x):
return jnp.roll(z, -2, axis=-1).at[-2:].set(new_x)
@jit
def RK4_ode(y0, z0, t, params, kp):
dt = t[1] - t[0]
def body_fun(carry, ts_i):
y_prev, z, i = carry
Qf, V, cf = kp
cfi = cf[i]
current_kp = [Qf, V, cfi]
new_x_half = y_prev + (dt / 2) * ode_nn(params, y_prev, cfi, current_kp, z)
z_updated_half = update_z(z, new_x_half)
k1 = ode_nn(params, y_prev,cfi, current_kp, z)
k2 = ode_nn(params, y_prev + 0.5 * dt * k1,cfi, current_kp, z_updated_half)
k3 = ode_nn(params, y_prev + 0.5 * dt * k2,cfi, current_kp, z_updated_half)
new_x_full = y_prev + dt * k3
z_updated_full = update_z(z, new_x_full)
k4 = ode_nn(params, y_prev + dt * k3,cfi, current_kp, z_updated_full)
y_next = y_prev + dt * (k1 + 2. * k2 + 2. * k3 + k4) / 6.
z_next = update_z(z, y_next)
return (y_next, z_next, i + 1), y_next
_, y = lax.scan(body_fun, (y0, z0, 0), jnp.zeros(len(t) - 1))
return jnp.vstack([y0[None, :], y])
@jit
def loss_fn(params, x0,z0, t_span, true_solution, kp):
learned_solution = RK4_ode(x0,z0, t_span, params, kp)
return jnp.sum((learned_solution - true_solution) ** 2)
num_steps = 100*5
t_span_true = jnp.linspace(0., 200, num_steps)
x0_true = jnp.array([0.3, 0.2])
z0_true = jnp.array([0.,0.])
true_solution = [A,B]
true_solution_j = jnp.array(true_solution)
true_solution_jax = true_solution_j.T
kp = [0.6, 15, cfa]
layer_sizes = [3 + z0_true.size, 8, 8, 2]
def init_network_params(layer_sizes, rng_key):
params = []
for n_in, n_out in zip(layer_sizes[:-1], layer_sizes[1:]):
W_key, b_key = jax.random.split(rng_key)
W = 1e-2 * jax.random.normal(W_key, (n_out, n_in))
b = 1e-2 * jax.random.normal(b_key, (n_out,))
params.append((W, b))
return params
rng = jax.random.PRNGKey(0)
params = init_network_params(layer_sizes, rng)
grad_loss = grad(loss_fn)
opt_init, opt_update, get_params = adam(1e-2)
opt_state = opt_init(params)
loss = []
for i in range(2000):
grads = grad_loss(get_params(opt_state), x0_true, z0_true, t_span_true, true_solution_jax, kp)
opt_state = opt_update(i, grads, opt_state)
if i % 100 == 0:
loss_value = loss_fn(get_params(opt_state), x0_true, z0_true, t_span_true, true_solution_jax, kp)
print(f"Iteration {i}, Loss: {loss_value:.4f}")
loss.append(loss_value)
params_opt = get_params(opt_state)
print("Optimized parameters:", params_opt)
pdf = PdfPages('Train_mean.pdf')
true_solution = [A,B]
true_solution_j = jnp.array(true_solution)
true_solution_jax = true_solution_j.T
kp = [0.6, 15, cfa]
fig, axs = plt.subplots(4, 1, figsize=(9, 10)) # 2 rows, 1 column
fitted_solution = RK4_ode(x0_true, z0_true, t_span_true, params_opt, kp)
# Plot A_true and A on the first subplot
axs[0].plot(np.linspace(0,200,len(true_solution[0])), true_solution_j[0], 'r', label="Plant")
axs[0].plot(np.linspace(0,200,len(fitted_solution.T[0])), fitted_solution.T[0],'k',label="Struc")
axs[0].set_ylabel(r"$c_A$")
# Plot B_true (assuming this is the same as the true B) and B on the second subplot
axs[1].plot(np.linspace(0,200,len(true_solution[1])), true_solution_j[1], 'g', label="Plant")
axs[1].plot(np.linspace(0,200,len(fitted_solution.T[1])), fitted_solution.T[1],'k',label="Struc")
axs[1].set_ylabel(r"$c_B$")
axs[2].plot(np.linspace(0,200,len(cfa)), cfa, 'k', label = r"$c_{Af}$")
axs[2].set_xlabel("Time (min)")
axs[2].set_ylabel(r"$c_{Af}$")
axs[3].semilogy(np.linspace(0,1000,len(loss)), loss, 'b', label = "SSE")
axs[3].set_xlabel("Number of iterations")
axs[3].set_ylabel("SSE")
plt.tight_layout()
pdf.savefig(fig) # Save the current figure into the PDF
plt.close(fig)
t_start = 0
t_end = 200
num_step_ups = 5
t_jumps = t_end/num_step_ups
A,B,C, ta, cfa = [],[],[],[],[]
initial_conditions = [0.3, 0.2, 0.1]
np.random.seed(500)
parameters = [[0.6, 15, 0.3, 0.2, 0.5, 0.1]]
parameters = [list(inner_list) for inner_list in parameters for _ in range(5)]
for i in range(len(parameters)-1):
parameters[i+1][2] = np.random.uniform(0,1.5)
for i, params in enumerate(parameters):
t_start = t_jumps * i
t_end = t_jumps * (i + 1)
t = np.linspace(t_start, t_end, 100)
sol = solve_ivp(Reactor, [t_start, t_end], initial_conditions, method="RK45", t_eval=t, args=params)
initial_conditions = sol.y[:, -1]
A.append(sol.y[0])
B.append(sol.y[1])
C.append(sol.y[2])
ta.append(t)
A = np.concatenate(A)
B = np.concatenate(B)
C = np.concatenate(C)
A = A + np.random.normal(0,0.01,len(A))
B = B + np.random.normal(0,0.01,len(B))
C = C + np.random.normal(0,0.01,len(C))
t = np.concatenate(ta)
for i in range(len(parameters)):
cfa.append([parameters[i][2]]*100)
cfa = np.concatenate(cfa)
true_solution = [A,B]
true_solution_j = jnp.array(true_solution)
true_solution_jax = true_solution_j.T
kp = [0.6, 15, cfa]
x0_true = jnp.array([0.3,0.2])
fig, axs = plt.subplots(3, 1, figsize=(9, 10)) # 2 rows, 1 column
fitted_solution = RK4_ode(x0_true, z0_true, t_span_true, params_opt, kp)
# Plot A_true and A on the first subplot
axs[0].plot(np.linspace(0,200,len(true_solution[0])), true_solution_j[0], 'r', label="Plant")
axs[0].plot(np.linspace(0,200,len(fitted_solution.T[0])), fitted_solution.T[0],'k',label="Struc")
axs[0].set_ylabel(r"$c_A$")
# Plot B_true (assuming this is the same as the true B) and B on the second subplot
axs[1].plot(np.linspace(0,200,len(true_solution[1])), true_solution_j[1], 'g', label="Plant")
axs[1].plot(np.linspace(0,200,len(fitted_solution.T[1])), fitted_solution.T[1],'k',label="Struc")
axs[1].set_ylabel(r"$c_B$")
axs[2].plot(np.linspace(0,200,len(cfa)), cfa, 'k', label = r"$c_{Af}$")
axs[2].set_xlabel("Time (min)")
axs[2].set_ylabel(r"$c_{Af}$")
#axs[2].plot(C, 'k', label = r"$c_{Af}$")
#axs[2].set_xlabel("Time (min)")
#axs[2].set_ylabel(r"$c_{C}$")
plt.tight_layout()
pdf.savefig(fig)
plt.close(fig)
pdf.close()