diff --git a/examples/wave_equation/README.md b/examples/wave_equation/README.md
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+## 1D Wave Equation
+Given the wave equation:
+
+$$ u_{tt} = c^2 u_{xx}, $$
+
+with wave speed $c = 1$, domain $x \in [0, 1]$ and $t \in [0, 1]$, and the initial and boundary conditions:
+$$u(0, x) = \sin(\pi x), $$
+$$u_t(0, x) = 0, $$
+$$u(t, 0) = 0,$$
+$$u(t, 1) = 0.$$
+
+The exact solution is $u(x, t) = \sin(\pi x) \cos(\pi t)$.
+
+### Problem Setup
+
+| 1D Wave Equation | |
+|------------------------------|---|
+| PDE equation | $f = u_{tt} - c^2 u_{xx}$ |
+| Initial condition (displacement) | $u(0, x) = \sin(\pi x)$ |
+| Initial condition (velocity) | $u_t(0, x) = 0$ |
+| Dirichlet boundary conditions | $u(t, 0) = u(t, 1) = 0$ |
+| The output of net | $[u(t, x)]$ |
+| Layers of net | $[2] + 5 \times [50] + [1]$ |
+| Sample count from collection points | $10000$ |
+| Sample count from initial conditions | $100$ |
+| Sample count from boundary conditions | $100$ |
+| Loss function | $\text{MSE}_0 + \text{MSE}_b + \text{MSE}_c$ |
diff --git a/examples/wave_equation/configs/config.yaml b/examples/wave_equation/configs/config.yaml
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+++ b/examples/wave_equation/configs/config.yaml
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+defaults:
+  - train
+  - _self_
+
+N0: 100
+N_b: 100
+N_f: 10_000
+
+time_domain:
+  _target_: pinnstf2.data.TimeDomain
+  t_interval: [0, 1.0]
+  t_points: 201
+
+spatial_domain:
+  _target_: pinnstf2.data.Interval
+  x_interval: [0, 1]
+  shape: [256, 1]
+
+mesh:
+  _target_: pinnstf2.data.Mesh
+  root_dir: null
+  read_data_fn: ???
+  ub: [1.0, 1.0]
+  lb: [0.0, 0.0]
+
+train_datasets:
+  - mesh_sampler:
+      _target_: pinnstf2.data.MeshSampler
+      _partial_: true
+      num_sample: ${N_f}
+      collection_points:
+        - f
+
+  - initial_condition:
+      _target_: pinnstf2.data.InitialCondition
+      _partial_: true
+      num_sample: ${N0}
+      solution:
+        - u
+        - v
+
+  - dirichlet_boundary_condition:
+      _target_: pinnstf2.data.DirichletBoundaryCondition
+      _partial_: true
+      num_sample: ${N_b}
+      solution:
+        - u
+
+val_dataset:
+  - mesh_sampler:
+      _target_: pinnstf2.data.MeshSampler
+      _partial_: true
+      solution:
+        - u
+
+pred_dataset:
+  - mesh_sampler:
+      _target_: pinnstf2.data.MeshSampler
+      _partial_: true
+      solution:
+        - u
+
+net:
+  _target_: pinnstf2.models.FCN
+  layers: [2, 50, 50, 50, 50, 50, 1]
+  output_names:
+    - u
+
+trainer:
+  max_epochs: 20000
+  check_val_every_n_epoch: 20001
+
+model:
+  loss_fn: mse
+
+train: true
+val: true
+test: false
+optimized_metric:
+  error:
+    - u
+
+plotting: null
+
+seed: 1234
+task_name: wave_equation
+
+hydra:
+  searchpath:
+    - pkg://pinnstf2/conf
diff --git a/examples/wave_equation/train.py b/examples/wave_equation/train.py
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+++ b/examples/wave_equation/train.py
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+from typing import Dict, Optional
+
+import hydra
+import numpy as np
+import tensorflow as tf
+from omegaconf import DictConfig
+
+import pinnstf2
+
+
+C = 1.0  # wave speed
+
+
+def read_data_fn(root_path):
+    """Generate analytical reference data for the 1D wave equation.
+
+    Exact solution: u(x, t) = sin(pi*x) * cos(pi*c*t)
+    satisfying u_tt = c^2 * u_xx with u(0,t) = u(1,t) = 0.
+
+    :param root_path: The root directory containing the data (unused for analytical data).
+    :return: Dictionary with exact solution array.
+    """
+
+    nx, nt = 256, 201
+    x = np.linspace(0, 1, nx)
+    t = np.linspace(0, 1, nt)
+    X, T = np.meshgrid(x, t, indexing="ij")
+    exact_u = np.sin(np.pi * X) * np.cos(np.pi * C * T)
+    return {"u": exact_u}
+
+
+def pde_fn(
+    outputs: Dict[str, tf.Tensor],
+    x: tf.Tensor,
+    t: tf.Tensor,
+):
+    """Define the wave equation PDE residual: u_tt - c^2 * u_xx = 0.
+
+    :param outputs: Dictionary of network outputs.
+    :param x: Spatial coordinate tensor.
+    :param t: Temporal coordinate tensor.
+    :return: Updated outputs with PDE residual.
+    """
+
+    u_x, u_t = pinnstf2.utils.gradient(outputs["u"], [x, t])
+    u_xx = pinnstf2.utils.gradient(u_x, x)
+    u_tt = pinnstf2.utils.gradient(u_t, t)
+    outputs["f"] = u_tt - C**2 * u_xx
+    return outputs
+
+
+def output_fn(
+    outputs: Dict[str, tf.Tensor],
+    x: tf.Tensor,
+    t: tf.Tensor,
+):
+    """Compute velocity field for initial velocity enforcement.
+
+    :param outputs: Dictionary of network outputs.
+    :param x: Spatial coordinate tensor.
+    :param t: Temporal coordinate tensor.
+    :return: Updated outputs with velocity field.
+    """
+
+    outputs["v"] = pinnstf2.utils.gradient(outputs["u"], t)
+    return outputs
+
+
+@hydra.main(version_base="1.3", config_path="configs", config_name="config.yaml")
+def main(cfg: DictConfig) -> Optional[float]:
+    """Main entry point for training.
+
+    :param cfg: DictConfig configuration composed by Hydra.
+    :return: Optional[float] with optimized metric value.
+    """
+
+    pinnstf2.utils.extras(cfg)
+
+    metric_dict, _ = pinnstf2.train(
+        cfg, read_data_fn=read_data_fn, pde_fn=pde_fn, output_fn=output_fn
+    )
+
+    metric_value = pinnstf2.utils.get_metric_value(
+        metric_dict=metric_dict, metric_names=cfg.get("optimized_metric")
+    )
+
+    return metric_value
+
+
+if __name__ == "__main__":
+    main()