diff --git a/examples-gallery/plot_non_contiguous_parameter_estimation.py b/examples-gallery/plot_non_contiguous_parameter_estimation.py
new file mode 100644
index 00000000..c6f98fb4
--- /dev/null
+++ b/examples-gallery/plot_non_contiguous_parameter_estimation.py
@@ -0,0 +1,209 @@
+# %%
+"""
+
+Parameter Identification from Non-Contiguous Measurements.
+==========================================================
+
+For parameter estimation it is common to collect measurements of a system's
+trajectories for distinct experiments. for example, if you are identifying the
+parameters of a mass-spring-damper system, you will exite the system with
+different initial conditions multiple times. The date cannot simply be stacked
+and the identification run because the measurement data would be discontinuous
+between trials.
+A work around in opty is to creat a set of differential equations with unique
+state variables. For each measurement trial that all share the same constant
+parameters. You can then identify the parameters from all measurement trials
+simultaneously by passing the uncoupled differential equations to opty.
+
+For exaple:
+Four measurements of the location of a simple system consisting of a mass
+connected to a fixed point by a spring and a damper are done. The movement
+is in horizontal direction. The the spring constant and the damping coefficient
+will be identified.
+
+
+**State Variables**
+
+- :math:`x_1`: position of the mass of the first measurement trial [m]
+- :math:`x_2`: position of the mass of the second measurement trial [m]
+- :math:`x_3`: position of the mass of the third measurement trial [m]
+- :math:`x_4`: position of the mass of the fourth measurement trial [m]
+- :math:`u_1`: speed of the mass of the first measurement trial [m/s]
+- :math:`u_2`: speed of the mass of the second measurement trial [m/s]
+- :math:`u_3`: speed of the mass of the third measurement trial [m/s]
+- :math:`u_4`: speed of the mass of the fourth measurement trial [m/s]
+
+**Parameters**
+
+- :math:`m`: mass for both systems system [kg]
+- :math:`c`: damping coefficient for both systems [Ns/m]
+- :math:`k`: spring constant for both systems [N/m]
+- :math:`l_0`: natural length of the spring [m]
+
+"""
+# %%
+# Set up the equations of motion and integrate them to get the measurements.
+#
+import sympy as sm
+import numpy as np
+import sympy.physics.mechanics as me
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+from opty import Problem
+from opty.utils import parse_free
+
+x1, x2, x3, x4, u1, u2, u3, u4 = me.dynamicsymbols('x1, x2, x3, x4, u1, u2, u3, u4')
+m, c, k, l0 = sm.symbols('m, c, k, l0')
+t = me.dynamicsymbols._t
+
+eom = sm.Matrix([
+    x1.diff(t) - u1,
+    x2.diff(t) - u2,
+    x3.diff(t) - u3,
+    x4.diff(t) - u4,
+    m*u1.diff(t) + c*u1 + k*(x1 - l0),
+    m*u2.diff(t) + c*u2 + k*(x2 - l0),
+    m*u3.diff(t) + c*u3 + k*(x3 - l0),
+    m*u4.diff(t) + c*u4 + k*(x4 - l0),
+])
+# %%
+# Equations of motion.
+sm.pprint(eom)
+
+# %%
+# Create the measurements for this example.
+#
+rhs = np.array([
+    u1,
+    u2,
+    u3,
+    u4,
+    1/m * (-c*u1 - k*(x1 - l0)),
+    1/m * (-c*u2 - k*(x2 - l0)),
+    1/m * (-c*u3 - k*(x3 - l0)),
+    1/m * (-c*u4 - k*(x4 - l0)),
+])
+
+qL = [x1, x2, x3, x4, u1, u2, u3, u4]
+pL = [m, c, k, l0]
+
+rhs_lam = sm.lambdify(qL + pL, rhs)
+
+def gradient(t, x, args):
+    return rhs_lam(*x, *args).reshape(8)
+
+t0, tf = 0, 20
+num_nodes = 500
+times = np.linspace(t0, tf, num_nodes)
+t_span = (t0, tf)
+
+x0 = np.array([2, 3, 4, 5, 0, 0, 0, 0])
+pL_vals = [1.0, 0.25, 1.0, 1.0]
+
+resultat1 = solve_ivp(gradient, t_span, x0, t_eval = times, args=(pL_vals,))
+resultat = resultat1.y.T
+
+measurements = []
+np.random.seed(123)
+for i in range(4):
+    measurements.append(resultat[:, i] + np.random.randn(resultat.shape[0]) * 0.5 +
+        np.random.randn(1)*2)
+
+# %%
+# Set up the Identification Problem.
+# --------------------------------
+#
+# If some measurement is considered more reliable, its weight w can be increased.
+#
+# objective = :math:`\int_{t_0}^{t_f} (w_1 (x_1 - x_1^m)^2 + w_2 (x_2 - x_2^m)^2 + w_3 (x_3 - x_3^m)^2 + w_4 (x_4 - x_4^m)^2)\, dt`
+#
+state_symbols = [x1, x2, x3, x4, u1, u2, u3, u4]
+unknown_parameters = [c, k]
+
+interval_value = (tf - t0) / (num_nodes - 1)
+par_map = {m: pL_vals[0], l0: pL_vals[3]}
+
+w =[1, 1, 1, 1]
+def obj(free):
+    return interval_value *np.sum((w[0] * free[:num_nodes] - measurements[0])**2 +
+            w[1] * (free[num_nodes:2*num_nodes] - measurements[1])**2 +
+            w[2] * (free[2*num_nodes:3*num_nodes] - measurements[2])**2 +
+            w[3] * (free[3*num_nodes:4*num_nodes] - measurements[3])**2
+)
+
+
+def obj_grad(free):
+    grad = np.zeros_like(free)
+    grad[:num_nodes] = 2 * w[0] * interval_value * (free[:num_nodes] -
+                            measurements[0])
+    grad[num_nodes:2*num_nodes] = 2 * w[1] * (interval_value *
+                    (free[num_nodes:2*num_nodes] - measurements[1]))
+    grad[2*num_nodes:3*num_nodes] = 2 * w[2] * (interval_value *
+                    (free[2*num_nodes:3*num_nodes] - measurements[2]))
+    grad[3*num_nodes:4*num_nodes] = 2 * w[3] * (interval_value *
+                    (free[3*num_nodes:4*num_nodes] - measurements[3]))
+    return grad
+
+
+instance_constraints = (
+    x1.subs({t: t0}) - x0[0],
+    x2.subs({t: t0}) - x0[1],
+    x3.subs({t: t0}) - x0[2],
+    x4.subs({t: t0}) - x0[3],
+    u1.subs({t: t0}) - x0[4],
+    u2.subs({t: t0}) - x0[5],
+    u3.subs({t: t0}) - x0[6],
+    u4.subs({t: t0}) - x0[7],
+)
+
+bounds = {
+    c: (0, 2),
+    k: (1, 3)
+}
+
+problem = Problem(
+    obj,
+    obj_grad,
+    eom,
+    state_symbols,
+    num_nodes,
+    interval_value,
+    known_parameter_map=par_map,
+    bounds=bounds,
+    time_symbol=me.dynamicsymbols._t,
+)
+
+# %%
+# Initial guess.
+#
+initial_guess = np.array(list(measurements[0]) + list(measurements[1]) +
+    list(measurements[2]) +list(measurements[3]) + list(np.zeros(4*num_nodes))
+    + [0.1, 0.1])
+
+# %%
+# Solve the Optimization Problem.
+#
+solution, info = problem.solve(initial_guess)
+print(info['status_msg'])
+problem.plot_objective_value()
+# %%
+problem.plot_constraint_violations(solution)
+# %%
+# Results obtained.
+#------------------
+#
+print(f'Estimate of damping parameter is  {solution[-2]:.2f}')
+print(f'Estimate ofthe spring constant is {solution[-1]:.2f}')
+
+# %%
+fig, ax = plt.subplots(4, 1, figsize=(8, 8), sharex=True)
+for i in range(4):
+    ax[i].plot(times, measurements[i], label=str(qL[i]))
+    ax[i].set_ylabel(f'measurement {i+1}')
+ax[0].set_title('Measurements')
+ax[-1].set_xlabel('Time [sec]')
+prevent_output = 0
+
+# %%
+problem.plot_trajectories(solution)
+plt.show()
\ No newline at end of file
diff --git a/examples-gallery/plot_parameter_estimation.py b/examples-gallery/plot_parameter_estimation.py
new file mode 100644
index 00000000..0a29908b
--- /dev/null
+++ b/examples-gallery/plot_parameter_estimation.py
@@ -0,0 +1,209 @@
+# %%
+"""
+
+Parameter Identification of a Mass-Spring-Damper System.
+========================================================
+
+For parameter estimation it is common to collect measurements of a system's
+trajectories for distinct experiments. for example, if you are identifying the
+parameters of a mass-spring-damper system, you will exite the system with
+different initial conditions multiple times. The date cannot simply be stacked
+and the identification run because the measurement data would be discontinuous
+between trials.
+A work around in opty is to creat a set of differential equations with unique
+state variables. For each measurement trial that all share the same constant
+parameters. You can then identify the parameters from all measurement trials
+simultaneously by passing the uncoupled differential equations to opty.
+
+For exaple:
+Four measurements of the location of a simple system consisting of a mass
+connected to a fixed point by a spring and a damper are done. The movement
+is in horizontal direction. The the spring constant and the damping coefficient
+will be identified.
+
+
+**State Variables**
+
+- :math:`x_1`: position of the mass of the first measurement trial [m]
+- :math:`x_2`: position of the mass of the second measurement trial [m]
+- :math:`x_3`: position of the mass of the third measurement trial [m]
+- :math:`x_4`: position of the mass of the fourth measurement trial [m]
+- :math:`u_1`: speed of the mass of the first measurement trial [m/s]
+- :math:`u_2`: speed of the mass of the second measurement trial [m/s]
+- :math:`u_3`: speed of the mass of the third measurement trial [m/s]
+- :math:`u_4`: speed of the mass of the fourth measurement trial [m/s]
+
+**Parameters**
+
+- :math:`m`: mass for both systems system [kg]
+- :math:`c`: damping coefficient for both systems [Ns/m]
+- :math:`k`: spring constant for both systems [N/m]
+- :math:`l_0`: natural length of the spring [m]
+
+"""
+# %%
+# Set up the equations of motion and integrate them to get the measurements.
+#
+import sympy as sm
+import numpy as np
+import sympy.physics.mechanics as me
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+from opty import Problem
+from opty.utils import parse_free
+
+x1, x2, x3, x4, u1, u2, u3, u4 = me.dynamicsymbols('x1, x2, x3, x4, u1, u2, u3, u4')
+m, c, k, l0 = sm.symbols('m, c, k, l0')
+t = me.dynamicsymbols._t
+
+eom = sm.Matrix([
+    x1.diff(t) - u1,
+    x2.diff(t) - u2,
+    x3.diff(t) - u3,
+    x4.diff(t) - u4,
+    m*u1.diff(t) + c*u1 + k*(x1 - l0),
+    m*u2.diff(t) + c*u2 + k*(x2 - l0),
+    m*u3.diff(t) + c*u3 + k*(x3 - l0),
+    m*u4.diff(t) + c*u4 + k*(x4 - l0),
+])
+# %%
+# Equations of motion.
+sm.pprint(eom)
+
+# %%
+# Create the measurements for this example.
+#
+rhs = np.array([
+    u1,
+    u2,
+    u3,
+    u4,
+    1/m * (-c*u1 - k*(x1 - l0)),
+    1/m * (-c*u2 - k*(x2 - l0)),
+    1/m * (-c*u3 - k*(x3 - l0)),
+    1/m * (-c*u4 - k*(x4 - l0)),
+])
+
+qL = [x1, x2, x3, x4, u1, u2, u3, u4]
+pL = [m, c, k, l0]
+
+rhs_lam = sm.lambdify(qL + pL, rhs)
+
+def gradient(t, x, args):
+    return rhs_lam(*x, *args).reshape(8)
+
+t0, tf = 0, 20
+num_nodes = 500
+times = np.linspace(t0, tf, num_nodes)
+t_span = (t0, tf)
+
+x0 = np.array([2, 3, 4, 5, 0, 0, 0, 0])
+pL_vals = [1.0, 0.25, 1.0, 1.0]
+
+resultat1 = solve_ivp(gradient, t_span, x0, t_eval = times, args=(pL_vals,))
+resultat = resultat1.y.T
+
+measurements = []
+np.random.seed(123)
+for i in range(4):
+    measurements.append(resultat[:, i] + np.random.randn(resultat.shape[0]) * 0.5 +
+        np.random.randn(1)*2)
+
+# %%
+# Set up the Identification Problem.
+# --------------------------------
+#
+# If some measurement is considered more reliable, its weight w can be increased.
+#
+# objective = :math:`\int_{t_0}^{t_f} (w_1 (x_1 - x_1^m)^2 + w_2 (x_2 - x_2^m)^2 + w_3 (x_3 - x_3^m)^2 + w_4 (x_4 - x_4^m)^2)\, dt`
+#
+state_symbols = [x1, x2, x3, x4, u1, u2, u3, u4]
+unknown_parameters = [c, k]
+
+interval_value = (tf - t0) / (num_nodes - 1)
+par_map = {m: pL_vals[0], l0: pL_vals[3]}
+
+w =[1, 1, 1, 1]
+def obj(free):
+    return interval_value *np.sum((w[0] * free[:num_nodes] - measurements[0])**2 +
+            w[1] * (free[num_nodes:2*num_nodes] - measurements[1])**2 +
+            w[2] * (free[2*num_nodes:3*num_nodes] - measurements[2])**2 +
+            w[3] * (free[3*num_nodes:4*num_nodes] - measurements[3])**2
+)
+
+
+def obj_grad(free):
+    grad = np.zeros_like(free)
+    grad[:num_nodes] = 2 * w[0] * interval_value * (free[:num_nodes] -
+                            measurements[0])
+    grad[num_nodes:2*num_nodes] = 2 * w[1] * (interval_value *
+                    (free[num_nodes:2*num_nodes] - measurements[1]))
+    grad[2*num_nodes:3*num_nodes] = 2 * w[2] * (interval_value *
+                    (free[2*num_nodes:3*num_nodes] - measurements[2]))
+    grad[3*num_nodes:4*num_nodes] = 2 * w[3] * (interval_value *
+                    (free[3*num_nodes:4*num_nodes] - measurements[3]))
+    return grad
+
+
+instance_constraints = (
+    x1.subs({t: t0}) - x0[0],
+    x2.subs({t: t0}) - x0[1],
+    x3.subs({t: t0}) - x0[2],
+    x4.subs({t: t0}) - x0[3],
+    u1.subs({t: t0}) - x0[4],
+    u2.subs({t: t0}) - x0[5],
+    u3.subs({t: t0}) - x0[6],
+    u4.subs({t: t0}) - x0[7],
+)
+
+bounds = {
+    c: (0, 2),
+    k: (1, 3)
+}
+
+problem = Problem(
+    obj,
+    obj_grad,
+    eom,
+    state_symbols,
+    num_nodes,
+    interval_value,
+    known_parameter_map=par_map,
+    bounds=bounds,
+    time_symbol=me.dynamicsymbols._t,
+)
+
+# %%
+# Initial guess.
+#
+initial_guess = np.array(list(measurements[0]) + list(measurements[1]) +
+    list(measurements[2]) +list(measurements[3]) + list(np.zeros(4*num_nodes))
+    + [0.1, 0.1])
+
+# %%
+# Solve the Optimization Problem.
+#
+solution, info = problem.solve(initial_guess)
+print(info['status_msg'])
+problem.plot_objective_value()
+# %%
+problem.plot_constraint_violations(solution)
+# %%
+# Results obtained.
+#------------------
+#
+print(f'Estimate of damping parameter is  {solution[-2]:.2f}')
+print(f'Estimate ofthe spring constant is {solution[-1]:.2f}')
+
+# %%
+fig, ax = plt.subplots(4, 1, figsize=(8, 8), sharex=True)
+for i in range(4):
+    ax[i].plot(times, measurements[i], label=str(qL[i]))
+    ax[i].set_ylabel(f'measurement {i+1}')
+ax[0].set_title('Measurements')
+ax[-1].set_xlabel('Time [sec]')
+prevent_output = 0
+
+# %%
+problem.plot_trajectories(solution)
+plt.show()
\ No newline at end of file