SYMBOL INDEX (191 symbols across 12 files)

FILE: GrayScott2D/models_tf.py
  class Sampler (line 13) | class Sampler:
    method __init__ (line 15) | def __init__(self, dim, coords, func, name=None):
    method sample (line 21) | def sample(self, N):
  class ResidualSampler (line 27) | class ResidualSampler:
    method __init__ (line 29) | def __init__(self, X, name=None):
    method sample (line 33) | def sample(self, batch_size):
  class DataSampler (line 39) | class DataSampler:
    method __init__ (line 41) | def __init__(self, X, Y, name=None):
    method sample (line 46) | def sample(self, batch_size):
  class Gray_Scott2D (line 53) | class Gray_Scott2D:
    method __init__ (line 55) | def __init__(self, data_sampler, residual_sampler, layers, b, d):
    method initialize_NN (line 146) | def initialize_NN(self, layers):
    method xavier_init (line 157) | def xavier_init(self, size):
    method neural_net (line 163) | def neural_net(self, H):
    method net_u (line 174) | def net_u(self, t, x, y):
    method net_r (line 186) | def net_r(self, t, x, y):
    method fetch_minibatch_data (line 208) | def fetch_minibatch_data(self, N):
    method fetch_minibatch_residual (line 213) | def fetch_minibatch_residual(self, N):
    method train (line 218) | def train(self, nIter=10000, batch_size=128):
    method predict (line 252) | def predict(self, X_star):
  class Gray_Scott2D_FF (line 262) | class Gray_Scott2D_FF:
    method __init__ (line 264) | def __init__(self, data_sampler, residual_sampler, layers, b, d):
    method xavier_init (line 367) | def xavier_init(self, size):
    method initialize_NN (line 373) | def initialize_NN(self, layers):
    method neural_net (line 391) | def neural_net(self, H):
    method net_u (line 416) | def net_u(self, t, x, y):
    method net_r (line 428) | def net_r(self, t, x, y):
    method fetch_minibatch_data (line 453) | def fetch_minibatch_data(self, N):
    method fetch_minibatch_residual (line 458) | def fetch_minibatch_residual(self, N):
    method train (line 463) | def train(self, nIter=10000, batch_size=128):
    method predict (line 498) | def predict(self, X_star):
  class Gray_Scott2D_ST_mFF (line 508) | class Gray_Scott2D_ST_mFF:
    method __init__ (line 510) | def __init__(self, data_sampler, residual_sampler, layers, b, d):
    method xavier_init (line 613) | def xavier_init(self, size):
    method initialize_NN (line 619) | def initialize_NN(self, layers):
    method neural_net (line 637) | def neural_net(self, H):
    method net_u (line 675) | def net_u(self, t, x, y):
    method net_r (line 687) | def net_r(self, t, x, y):
    method fetch_minibatch_data (line 709) | def fetch_minibatch_data(self, N):
    method fetch_minibatch_residual (line 714) | def fetch_minibatch_residual(self, N):
    method train (line 719) | def train(self, nIter=10000, batch_size=128):
    method predict (line 753) | def predict(self, X_star):

FILE: Poisson1D/Compute_Jacobian.py
  function jacobian (line 19) | def jacobian(output, inputs, use_pfor=True, parallel_iterations=None):

FILE: Poisson1D/Poisson_1D.py
  function u (line 23) | def u(x, a, b):
  function u_xx (line 27) | def u_xx(x, a, b):

FILE: Poisson1D/models_tf.py
  class Sampler (line 13) | class Sampler:
    method __init__ (line 15) | def __init__(self, dim, coords, func, name=None):
    method sample (line 21) | def sample(self, N):
  class NN (line 26) | class NN:
    method __init__ (line 27) | def __init__(self, layers, bcs_samplers, res_samplers, u, a, b, sigma):
    method xavier_init (line 104) | def xavier_init(self, size):
    method initialize_NN (line 112) | def initialize_NN(self, layers):
    method forward_pass (line 123) | def forward_pass(self, H):
    method net_u (line 136) | def net_u(self, x):
    method net_r (line 141) | def net_r(self, x):
    method fetch_minibatch (line 150) | def fetch_minibatch(self, sampler, N):
    method train (line 156) | def train(self, nIter=10000, batch_size=128, log_NTK=True, log_weights...
    method predict_u (line 196) | def predict_u(self, X_star):
    method predict_r (line 203) | def predict_r(self, X_star):
  class NN_FF (line 210) | class NN_FF:
    method __init__ (line 211) | def __init__(self, layers, bcs_samplers, res_samplers, u, a, b, sigma):
    method xavier_init (line 291) | def xavier_init(self, size):
    method initialize_NN (line 299) | def initialize_NN(self, layers):
    method forward_pass (line 310) | def forward_pass(self, H):
    method net_u (line 327) | def net_u(self, x):
    method net_r (line 332) | def net_r(self, x):
    method compute_jacobian (line 342) | def compute_jacobian(self, f):
    method compute_ntk (line 356) | def compute_ntk(self, J1_list, x1, J2_list, x2):
    method fetch_minibatch (line 369) | def fetch_minibatch(self, sampler, N):
    method train (line 375) | def train(self, nIter=10000, batch_size=128, log_NTK=True, log_weights...
    method predict_u (line 415) | def predict_u(self, X_star):
    method predict_r (line 422) | def predict_r(self, X_star):
  class NN_mFF (line 429) | class NN_mFF:
    method __init__ (line 430) | def __init__(self, layers, bcs_samplers, res_samplers, u,a, b, sigma):
    method xavier_init (line 509) | def xavier_init(self, size):
    method initialize_NN (line 517) | def initialize_NN(self, layers):
    method forward_pass (line 536) | def forward_pass(self, H):
    method net_u (line 561) | def net_u(self, x):
    method net_r (line 566) | def net_r(self, x):
    method fetch_minibatch (line 575) | def fetch_minibatch(self, sampler, N):
    method train (line 581) | def train(self, nIter=10000, batch_size=128, log_NTK=True, log_weights...
    method predict_u (line 621) | def predict_u(self, X_star):
    method predict_r (line 628) | def predict_r(self, X_star):

FILE: Regression/Compute_Jacobian.py
  function jacobian (line 19) | def jacobian(output, inputs, use_pfor=True, parallel_iterations=None):

FILE: Regression/models_tf.py
  class Sampler (line 14) | class Sampler:
    method __init__ (line 16) | def __init__(self, dim, coords, func, name=None):
    method sample (line 23) | def sample(self, N):
  class NN_FF (line 29) | class NN_FF:
    method __init__ (line 30) | def __init__(self, layers, X_u, Y_u, a, u, sigma):
    method xavier_init (line 126) | def xavier_init(self, size):
    method NTK_init (line 134) | def NTK_init(self, size):
    method initialize_NN (line 142) | def initialize_NN(self, layers):
    method forward_pass (line 154) | def forward_pass(self, H):
    method net_u (line 171) | def net_u(self, x):
    method compute_jacobian (line 176) | def compute_jacobian(self, f):
    method compute_ntk (line 189) | def compute_ntk(self, J1_list, x1, J2_list, x2):
    method fetch_minibatch (line 204) | def fetch_minibatch(self, sampler, N):
    method train (line 210) | def train(self, nIter=10000, log_NTK=True, log_weights=True):
    method predict_u (line 271) | def predict_u(self, X_star):

FILE: Regression/regression.py
  function u (line 18) | def u(x, a):
  function compute_weights_diff (line 88) | def compute_weights_diff(weights_1, weights_2):
  function compute_weights_norm (line 96) | def compute_weights_norm(weights, biases):

FILE: heat1D/heat1D.py
  function u (line 11) | def u(x, a, b):
  function u_t (line 21) | def u_t(x, a, b):
  function u_xx (line 24) | def u_xx(x, a, b):
  function f (line 27) | def f(x, a, b):
  function operator (line 32) | def operator(u, t, x, k,  sigma_t=1.0, sigma_x=1.0):

FILE: heat1D/models_tf.py
  class Sampler (line 13) | class Sampler:
    method __init__ (line 15) | def __init__(self, dim, coords, func, name = None):
    method sample (line 20) | def sample(self, N):
  class heat1D_NN (line 26) | class heat1D_NN:
    method __init__ (line 27) | def __init__(self, layers, operator, k, ics_sampler, bcs_sampler, res_...
    method xavier_init (line 113) | def xavier_init(self, size):
    method initialize_NN (line 120) | def initialize_NN(self, layers):
    method forward_pass (line 139) | def forward_pass(self, H):
    method net_u (line 153) | def net_u(self, t, x):
    method net_r (line 158) | def net_r(self, t, x):
    method fetch_minibatch (line 164) | def fetch_minibatch(self, sampler, N):
    method train (line 169) | def train(self, nIter=10000, batch_size=128):
    method predict_u (line 213) | def predict_u(self, X_star):
    method predict_r (line 220) | def predict_r(self, X_star):
  class heat1D_FF (line 227) | class heat1D_FF:
    method __init__ (line 228) | def __init__(self, layers, operator, k, ics_sampler, bcs_sampler, res_...
    method xavier_init (line 322) | def xavier_init(self, size):
    method initialize_NN (line 329) | def initialize_NN(self, layers):
    method forward_pass (line 348) | def forward_pass(self, H):
    method net_u (line 367) | def net_u(self, t, x):
    method net_r (line 372) | def net_r(self, t, x):
    method fetch_minibatch (line 378) | def fetch_minibatch(self, sampler, N):
    method train (line 383) | def train(self, nIter=10000, batch_size=128):
    method predict_u (line 427) | def predict_u(self, X_star):
    method predict_r (line 434) | def predict_r(self, X_star):
  class heat1D_ST_FF (line 442) | class heat1D_ST_FF:
    method __init__ (line 443) | def __init__(self, layers, operator, k, ics_sampler, bcs_sampler, res_...
    method xavier_init (line 539) | def xavier_init(self, size):
    method initialize_NN (line 547) | def initialize_NN(self, layers):
    method forward_pass (line 567) | def forward_pass(self, H):
    method net_u (line 595) | def net_u(self, t, x):
    method net_r (line 600) | def net_r(self, t, x):
    method fetch_minibatch (line 606) | def fetch_minibatch(self, sampler, N):
    method train (line 611) | def train(self, nIter=10000, batch_size=128):
    method predict_u (line 655) | def predict_u(self, X_star):
    method predict_r (line 662) | def predict_r(self, X_star):

FILE: wave1D/Compute_Jacobian.py
  function jacobian (line 19) | def jacobian(output, inputs, use_pfor=True, parallel_iterations=None):

FILE: wave1D/wave1D.py
  function u (line 9) | def u(x, a, c):
  function f (line 18) | def f(x, a, c):
  function operator (line 22) | def operator(u, t, x, c, sigma_t=1.0, sigma_x=1.0):

FILE: wave1D/wave_models_tf.py
  class Sampler (line 6) | class Sampler:
    method __init__ (line 8) | def __init__(self, dim, coords, func, name = None):
    method sample (line 13) | def sample(self, N):
  class Wave1D_NTK (line 19) | class Wave1D_NTK:
    method __init__ (line 23) | def __init__(self, layers, operator, ics_sampler, bcs_sampler, res_sam...
    method initialize_NN (line 161) | def initialize_NN(self, layers):
    method forward_pass (line 181) | def forward_pass(self, H, layers, weights, biases):
    method net_u (line 193) | def net_u(self, t, x):
    method net_u_t (line 200) | def net_u_t(self, t, x):
    method net_r (line 205) | def net_r(self, t, x):
    method compute_jacobian (line 214) | def compute_jacobian(self, f):
    method compute_ntk (line 227) | def compute_ntk(self, J1_list, D1, J2_list, D2):
    method fetch_minibatch (line 240) | def fetch_minibatch(self, sampler, N):
    method train (line 247) | def train(self, nIter=10000, batch_size=128, log_NTK=False, update_wei...
    method predict_u (line 330) | def predict_u(self, X_star):
    method predict_r (line 337) | def predict_r(self, X_star):
  class Wave1D_NTK_mFF (line 344) | class Wave1D_NTK_mFF:
    method __init__ (line 348) | def __init__(self, layers, operator, ics_sampler, bcs_sampler, res_sam...
    method xavier_init (line 492) | def xavier_init(self, size):
    method initialize_NN (line 499) | def initialize_NN(self, layers):
    method forward_pass (line 519) | def forward_pass(self, H):
    method net_u (line 545) | def net_u(self, t, x):
    method net_u_t (line 549) | def net_u_t(self, t, x):
    method net_r (line 554) | def net_r(self, t, x):
    method compute_jacobian (line 563) | def compute_jacobian(self, f):
    method compute_ntk (line 576) | def compute_ntk(self, J1_list, D1, J2_list, D2):
    method fetch_minibatch (line 589) | def fetch_minibatch(self, sampler, N):
    method train (line 595) | def train(self, nIter=10000, batch_size=128, log_NTK=False, update_wei...
    method predict_u (line 678) | def predict_u(self, X_star):
    method predict_r (line 685) | def predict_r(self, X_star):
  class Wave1D_NTK_ST_mFF (line 692) | class Wave1D_NTK_ST_mFF:
    method __init__ (line 694) | def __init__(self, layers, operator, ics_sampler, bcs_sampler, res_sam...
    method xavier_init (line 841) | def xavier_init(self, size):
    method initialize_NN (line 848) | def initialize_NN(self, layers):
    method forward_pass (line 867) | def forward_pass(self, H):
    method net_u (line 903) | def net_u(self, t, x):
    method net_u_t (line 907) | def net_u_t(self, t, x):
    method net_r (line 912) | def net_r(self, t, x):
    method compute_jacobian (line 921) | def compute_jacobian(self, f):
    method compute_ntk (line 934) | def compute_ntk(self, J1_list, D1, J2_list, D2):
    method fetch_minibatch (line 947) | def fetch_minibatch(self, sampler, N):
    method train (line 953) | def train(self, nIter=10000, batch_size=128, log_NTK=False, update_wei...
    method predict_u (line 1035) | def predict_u(self, X_star):
    method predict_r (line 1042) | def predict_r(self, X_star):