Merge branch 'main' of https://github.com/cmgcds/fastvpinns into main

examples/forward_problems_2d/complex_mesh/cd2d/cd2d_example.py

@@ -0,0 +1,80 @@
+# Example file for the poisson problem
+# Path: examples/poisson.py
+# file contains the exact solution, rhs and boundary conditions for the poisson problem
+import numpy as np
+import tensorflow as tf
+
+
+def left_boundary(x, y):
+    """
+    This function will return the boundary value for given component of a boundary
+    """
+    return 0.0
+
+
+def right_boundary(x, y):
+    """
+    This function will return the boundary value for given component of a boundary
+    """
+    val = 0.0
+    return val
+
+
+def top_boundary(x, y):
+    """
+    This function will return the boundary value for given component of a boundary
+    """
+    val = 0.0
+    return np.ones_like(x) * val
+
+
+def bottom_boundary(x, y):
+    """
+    This function will return the boundary value for given component of a boundary
+    """
+    return 0.0
+
+
+def rhs(x, y):
+    """
+    This function will return the value of the rhs at a given point
+    """
+    f_temp = 32 * (x * (1 - x) + y * (1 - y))
+
+    return f_temp
+
+
+def exact_solution(x, y):
+    """
+    This function will return the exact solution at a given point
+    """
+
+    val = 16 * x * (1 - x) * y * (1 - y)
+
+    return val
+
+
+def get_boundary_function_dict():
+    """
+    This function will return a dictionary of boundary functions
+    """
+    return {1000: bottom_boundary, 1001: right_boundary, 1002: top_boundary, 1003: left_boundary}
+
+
+def get_bound_cond_dict():
+    """
+    This function will return a dictionary of boundary conditions
+    """
+    return {1000: "dirichlet", 1001: "dirichlet", 1002: "dirichlet", 1003: "dirichlet"}
+
+
+def get_bilinear_params_dict():
+    """
+    This function will return a dictionary of bilinear parameters
+    """
+    eps = 1.0
+    b_x = 1.0
+    b_y = 0.0
+    c = 0.0
+
+    return {"eps": eps, "b_x": b_x, "b_y": b_y, "c": c}

examples/forward_problems_2d/complex_mesh/cd2d/input.yaml

@@ -0,0 +1,75 @@
+# This YAML file contains configuration parameters for a variational physics-informed neural network (VarPINN) experimentation.
+
+experimentation:
+  output_path: "output/cd2d/1"  # Path to the output directory where the results will be saved.
+
+geometry:
+  mesh_generation_method: "external"  # Method for generating the mesh. Can be "internal" or "external".
+  generate_mesh_plot: True  # Flag indicating whether to generate a plot of the mesh.
+
+  # internal mesh generated quadrilateral mesh, depending on the parameters specified below.
+
+  internal_mesh_params:  # Parameters for internal mesh generation method.
+    x_min: 0  # Minimum x-coordinate of the domain.
+    x_max: 1  # Maximum x-coordinate of the domain.
+    y_min: 0  # Minimum y-coordinate of the domain.
+    y_max: 1  # Maximum y-coordinate of the domain.
+    n_cells_x: 4  # Number of cells in the x-direction.
+    n_cells_y: 4  # Number of cells in the y-direction.
+    n_boundary_points: 400  # Number of boundary points.
+    n_test_points_x: 100  # Number of test points in the x-direction.
+    n_test_points_y: 100  # Number of test points in the y-direction.
+
+  exact_solution:
+    exact_solution_generation: "external" # whether the exact solution needs to be read from external file.
+    exact_solution_file_name: "" # External solution file name.
+
+  mesh_type: "quadrilateral"  # Type of mesh. Can be "quadrilateral" or other supported types.
+
+  external_mesh_params:  # Parameters for external mesh generation method.
+    mesh_file_name: "../meshes/circle_quad.mesh"  # Path to the external mesh file (should be a .mesh file).
+    boundary_refinement_level: 4  # Level of refinement for the boundary.
+    boundary_sampling_method: "lhs"  # Method for sampling the boundary. Can be "uniform" or "lhs".
+
+fe:
+  fe_order: 6 # Order of the finite element basis functions.
+  fe_type: "legendre"  # Type of finite element basis functions. Can be "jacobi" or other supported types.
+  quad_order: 10  # Order of the quadrature rule.
+  quad_type: "gauss-jacobi"  # Type of quadrature rule. Can be "gauss-jacobi" or other supported types.
+
+pde:
+  beta: 10  # Parameter for the PDE.
+
+model:
+  model_architecture: [2, 50,50,50,50, 1]  # Architecture of the neural network model.
+  activation: "tanh"  # Activation function used in the neural network.
+  use_attention: False  # Flag indicating whether to use attention mechanism in the model.
+  epochs: 50000  # Number of training epochs.
+  dtype: "float32"  # Data type used for computations.
+  set_memory_growth: False  # Flag indicating whether to set memory growth for GPU.
+
+  learning_rate:  # Parameters for learning rate scheduling.
+    initial_learning_rate: 0.001  # Initial learning rate.
+    use_lr_scheduler: False  # Flag indicating whether to use learning rate scheduler.
+    decay_steps: 1000  # Number of steps between each learning rate decay.
+    decay_rate: 0.99  # Decay rate for the learning rate.
+    staircase: False  # Flag indicating whether to use staircase decay.
+
+
+logging:
+  update_progress_bar: 100  # Number of steps between each update of the progress bar.
+  update_console_output: 5000  # Number of steps between each update of the console output.
+  update_solution_images: 10000  # Number of steps between each update of the intermediate solution images.
+  plot_residual_images: True
+  test_error_last_n_epochs: 1000
+
+wandb:
+  use_wandb: False  # Flag indicating whether to use Weights & Biases for logging.
+  project_name: "Volker_example_2"  # Name of the Weights & Biases project.
+  wandb_run_prefix: "without_scaling"  # Prefix for the Weights & Biases run.
+  entity: "starslab-iisc"  # Weights & Biases entity.
+
+additional:
+  run_by: "Thivin"  # Name of the person running the experiment.
+  System: "24"  # System identifier.
+