rename AC-inference to AC-discovery

Author: levimcclenny

examples/AC-assimilate.py renamed to examples/AC-discovery.py

@@ -13,35 +13,36 @@
 # Put params into a list
 params = [tf.Variable(0.0, dtype=tf.float32), tf.Variable(0.0, dtype=tf.float32)]
 
+
 # Define f_model, note the `vars` argument. Inputs must follow this order!
-def f_model(u_model, vars, x, t):
-    u = u_model(tf.concat([x,t],1))
+def f_model(u_model, var, x, t):
+    u = u_model(tf.concat([x, t], 1))
     u_x = tf.gradients(u, x)
     u_xx = tf.gradients(u_x, x)
-    u_t = tf.gradients(u,t)
-    c1 = vars[0] # tunable param 1
-    c2 = vars[1] # tunable param 2
-    f_u = u_t - c1*u_xx + c2*u*u*u - c2*u
+    u_t = tf.gradients(u, t)
+    c1 = var[0]  # tunable param 1
+    c2 = var[1]  # tunable param 2
+    f_u = u_t - c1 * u_xx + c2 * u * u * u - c2 * u
     return f_u
 
 
 # Import data, same data as Raissi et al
 
 data = scipy.io.loadmat('AC.mat')
 
-t = data['tt'].flatten()[:,None]
-x = data['x'].flatten()[:,None]
+t = data['tt'].flatten()[:, None]
+x = data['x'].flatten()[:, None]
 Exact = data['uu']
 Exact_u = np.real(Exact)
 
 # define MLP depth and layer width
 layer_sizes = [2, 128, 128, 128, 128, 1]
 
 # generate all combinations of x and t
-X, T = np.meshgrid(x,t)
+X, T = np.meshgrid(x, t)
 
-X_star = np.hstack((X.flatten()[:,None], T.flatten()[:,None]))
-u_star = Exact_u.T.flatten()[:,None]
+X_star = np.hstack((X.flatten()[:, None], T.flatten()[:, None]))
+u_star = Exact_u.T.flatten()[:, None]
 
 x = X_star[:, 0:1]
 t = X_star[:, 1:2]
@@ -50,16 +51,18 @@ def f_model(u_model, vars, x, t):
 # append to a list for input to model.fit
 X = [x, t]
 
-#define col_weights for SA discovery model
+# define col_weights for SA discovery model
 col_weights = tf.Variable(tf.random.uniform([np.shape(x)[0], 1]))
 
 # initialize, compile, train model
 model = DiscoveryModel()
-model.compile(layer_sizes, f_model, X, u_star, params, col_weights=col_weights) # baseline approach can be done by simply removing the col_weights arg
-model.tf_optimizer_weights = tf.keras.optimizers.Adam(lr=0.005, beta_1=.95) # an example as to how one could modify an optimizer, in this case the col_weights optimizer
+model.compile(layer_sizes, f_model, X, u_star, params,
+              col_weights=col_weights)  # baseline approach can be done by simply removing the col_weights arg
+model.tf_optimizer_weights = tf.keras.optimizers.Adam(lr=0.005,
+                                                      beta_1=.95)  # an example as to how one could modify an optimizer, in this case the col_weights optimizer
 
 # train loop
-model.fit(tf_iter = 10000)
+model.fit(tf_iter=10000)
 
 # doesnt work quite yet
-tdq.plotting.plot_weights(model, scale = 10.0)
+tdq.plotting.plot_weights(model, scale=10.0)