diff --git a/10-GD-2D-Adjoint.ipynb b/10-GD-2D-Adjoint.ipynb
index bdab990..2522ee0 100644
--- a/10-GD-2D-Adjoint.ipynb
+++ b/10-GD-2D-Adjoint.ipynb
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "60888470",
    "metadata": {},
    "outputs": [],
@@ -77,14 +77,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "015b49c7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "alpha_u = 1e-1\n",
-    "alpha_d = 1e-2\n",
-    "alpha_s = 1e-1"
+    "alpha_u = 0.\n",
+    "alpha_d = 0.0\n",
+    "alpha_s = 0. "
    ]
   },
   {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "dec1649f",
    "metadata": {},
    "outputs": [],
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "d020520c",
    "metadata": {},
    "outputs": [],
@@ -143,12 +143,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "2006c496",
    "metadata": {},
    "outputs": [],
    "source": [
-    "enable_disk_checkpointing()"
+    "# enable_disk_checkpointing()"
    ]
   },
   {
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "91bf008e",
    "metadata": {},
    "outputs": [],
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ea0c72b3",
    "metadata": {},
    "outputs": [],
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "68d0158c",
    "metadata": {},
    "outputs": [],
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "60795e15",
    "metadata": {},
    "outputs": [],
@@ -266,21 +266,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "5e06829e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Coefficient(WithGeometry(FunctionSpace(<firedrake.mesh.ExtrudedMeshTopology object at 0x155ff20d0>, TensorProductElement(FiniteElement('Lagrange', interval, 1), FiniteElement('Lagrange', interval, 1), cell=TensorProductCell(interval, interval)), name=None), Mesh(VectorElement(TensorProductElement(FiniteElement('Lagrange', interval, 1), FiniteElement('Lagrange', interval, 1), cell=TensorProductCell(interval, interval)), dim=2), 2)), 13)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Tic = Function(Q1, name=\"Initial Temperature\")\n",
     "Taverage = Function(Q1, name=\"Average Temperature\")\n",
@@ -298,28 +287,16 @@
    "id": "798ce6a6",
    "metadata": {},
    "source": [
-    "## Exercise \n",
-    "Visualise the "
+    "### <u>Exercise 10.1</u>\n",
+    "Visualise the initial condition."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "3adffcbb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=1024x768>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Let's visualise the final temperature field\n",
     "File(\"temp.pvd\").write(Tic)\n",
@@ -344,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "a0e7ec95",
    "metadata": {},
    "outputs": [],
@@ -377,7 +354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "650bada0",
    "metadata": {},
    "outputs": [],
@@ -417,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "f857a3d6",
    "metadata": {},
    "outputs": [],
@@ -443,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "09eb5a13",
    "metadata": {},
    "outputs": [],
@@ -452,7 +429,7 @@
     "T.project(Tic, bcs=energy_solver.strong_bcs)\n",
     "\n",
     "# Run the forward simulation\n",
-    "for timestep in range(max_timesteps-10, max_timesteps):\n",
+    "for timestep in range(max_timesteps-20, max_timesteps):\n",
     "    stokes_solver.solve()\n",
     "    energy_solver.solve()\n",
     "\n",
@@ -473,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "10a7f39c",
    "metadata": {},
    "outputs": [],
@@ -517,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "3fdf9731",
    "metadata": {},
    "outputs": [],
@@ -554,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "d5a5fdcb",
    "metadata": {},
    "outputs": [],
@@ -566,6 +543,38 @@
     "reduced_functional = ReducedFunctional(objective, control)\n"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "501a29f4",
+   "metadata": {},
+   "source": [
+    "### <u>Exercise 10.2</u>\n",
+    "In the following we can visualise the derivative, that we eventually pass on to optimisation routines. Compute the derivative associated to each term:\n",
+    "1. For `objective = t_misfit`. The derivative should show the sensitivity with respect to the <em>observed</em> final temperature field.\n",
+    "2. For `objective = alpha_u * (norm_obs * u_misfit / max_timesteps / norm_u_surface)`, showing the sensitivity with resect to surface velocities.\n",
+    "3. For `alpha_d * (norm_obs * damping / norm_damping)` showing how smoothing will act. \n",
+    "4. For `alpha_s * (norm_obs * smoothing / norm_smoothing)` showing how smoothing will act.\n",
+    "5. Repeat steps 1 and 2 for longer duration.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebdd16da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "derivative = reduced_functional.derivative(options={\"riesz_representation\":\"L2\"})\n",
+    "File(\"derivative.pvd\").write(derivative)\n",
+    "import pyvista as pv\n",
+    "temp_data = pv.read(\"derivative_0.vtu\")\n",
+    "plotter = pv.Plotter(notebook=True)\n",
+    "plotter.add_mesh(temp_data, cmap='seismic', clim=[-derivative.dat.data.max(), derivative.dat.data.max()],scalar_bar_args={'title': 'Solution', 'position_x': 0.2, 'position_y': 0.85})\n",
+    "plotter.camera_position = \"xy\"\n",
+    "plotter.show(jupyter_backend=\"static\", interactive=False)"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -589,27 +598,17 @@
    "id": "51f05957",
    "metadata": {},
    "source": [
-    "### Exercise 10.1\n",
+    "### <u>Exercise 10.3</u>\n",
     "1. Perform a Taylor test to make sure the derivatives you are calculating are correct. Plot the results against theoretical convergence rate. \n",
     "2. Is there anything you can do to stop the Taylor test yielding $O(2.0)$ results?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "f4016f9a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Taylor test\n",
-      "Computed residuals: [0.0011112257233898614, 0.00027780641843553076, 6.94516030575688e-05, 1.736290057043835e-05]\n",
-      "Computed convergence rates: [2.000000064457244, 2.0000000322249267, 2.0000000161157554]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "Delta_temp = Function(Tic.function_space(), name=\"Delta_Temperature\")\n",
@@ -617,38 +616,12 @@
     "minconv = taylor_test(reduced_functional, Tic, Delta_temp)\n"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "713d0c5d",
-   "metadata": {},
-   "source": [
-    "<button onclick=\"javascript:toggle_code()\">Show solution</button>\n",
-    "<script>\n",
-    "function toggle_code() {\n",
-    "    var code_cell = Jupyter.notebook.get_selected_index() + 1;\n",
-    "    Jupyter.notebook.get_cells()[code_cell].toggle();\n",
-    "}\n",
-    "</script>"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "id": "6dfcfcbf",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "computed_residuals = [0.0011112257233898614, 0.00027780641843553076, 6.94516030575688e-05, 1.736290057043835e-05]\n",
     "fig = plt.figure()\n",
@@ -684,40 +657,24 @@
    "execution_count": null,
    "id": "b9b7c118",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[20], line 25\u001b[0m\n\u001b[1;32m     20\u001b[0m optimiser \u001b[39m=\u001b[39m LinMoreOptimiser(\n\u001b[1;32m     21\u001b[0m     minimisation_problem,\n\u001b[1;32m     22\u001b[0m     minimisation_parameters,\n\u001b[1;32m     23\u001b[0m )\n\u001b[1;32m     24\u001b[0m optimiser\u001b[39m.\u001b[39madd_callback(callback)\n\u001b[0;32m---> 25\u001b[0m optimiser\u001b[39m.\u001b[39;49mrun()\n",
-      "File \u001b[0;32m~/Workplace/G-ADOPT/gadopt/inverse.py:229\u001b[0m, in \u001b[0;36mLinMoreOptimiser.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    224\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Run the actual ROL optimisation.\u001b[39;00m\n\u001b[1;32m    225\u001b[0m \n\u001b[1;32m    226\u001b[0m \u001b[39m    This will continue until the status test flags the optimisation to complete.\u001b[39;00m\n\u001b[1;32m    227\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrol_algorithm\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m    230\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrol_solver\u001b[39m.\u001b[39;49mrolvector,\n\u001b[1;32m    231\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrol_solver\u001b[39m.\u001b[39;49mrolobjective,\n\u001b[1;32m    232\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrol_solver\u001b[39m.\u001b[39;49mbounds,\n\u001b[1;32m    233\u001b[0m     )\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/pyadjoint/pyadjoint/optimization/rol_solver.py:38\u001b[0m, in \u001b[0;36mROLObjective.update\u001b[0;34m(self, x, flag, iteration)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(ROL, \u001b[39m\"\u001b[39m\u001b[39mUpdateType\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(flag, ROL\u001b[39m.\u001b[39mUpdateType):\n\u001b[1;32m     32\u001b[0m     \u001b[39m# Initial: has not been called before\u001b[39;00m\n\u001b[1;32m     33\u001b[0m     \u001b[39m# Accept: this is the new iterate, trial has been called\u001b[39;00m\n\u001b[1;32m     34\u001b[0m     \u001b[39m# Revert: revert to previous, trial has been called\u001b[39;00m\n\u001b[1;32m     35\u001b[0m     \u001b[39m# Trial: candidate for next\u001b[39;00m\n\u001b[1;32m     36\u001b[0m     \u001b[39m# Temp: temporary\u001b[39;00m\n\u001b[1;32m     37\u001b[0m     \u001b[39mif\u001b[39;00m flag \u001b[39min\u001b[39;00m [ROL\u001b[39m.\u001b[39mUpdateType\u001b[39m.\u001b[39mInitial, ROL\u001b[39m.\u001b[39mUpdateType\u001b[39m.\u001b[39mTrial, ROL\u001b[39m.\u001b[39mUpdateType\u001b[39m.\u001b[39mTemp]:\n\u001b[0;32m---> 38\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_val \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrf(x\u001b[39m.\u001b[39;49mdat)\n\u001b[1;32m     39\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tape_trial \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrf\u001b[39m.\u001b[39mtape\u001b[39m.\u001b[39mcheckpoint_block_vars(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrf\u001b[39m.\u001b[39mcontrols)\n\u001b[1;32m     40\u001b[0m     \u001b[39melif\u001b[39;00m flag \u001b[39m==\u001b[39m ROL\u001b[39m.\u001b[39mUpdateType\u001b[39m.\u001b[39mRevert:\n\u001b[1;32m     41\u001b[0m         \u001b[39m# revert back to the cached value\u001b[39;00m\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/pyadjoint/pyadjoint/tape.py:105\u001b[0m, in \u001b[0;36mno_annotations.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[39m@wraps\u001b[39m(function)\n\u001b[1;32m    103\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapper\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    104\u001b[0m     \u001b[39mwith\u001b[39;00m stop_annotating():\n\u001b[0;32m--> 105\u001b[0m         \u001b[39mreturn\u001b[39;00m function(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/pyadjoint/pyadjoint/reduced_functional.py:210\u001b[0m, in \u001b[0;36mReducedFunctional.__call__\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[39mwith\u001b[39;00m stop_annotating():\n\u001b[1;32m    207\u001b[0m         \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtape\u001b[39m.\u001b[39m_bar(\u001b[39m\"\u001b[39m\u001b[39mEvaluating functional\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39miter(\n\u001b[1;32m    208\u001b[0m             \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(blocks))\n\u001b[1;32m    209\u001b[0m         ):\n\u001b[0;32m--> 210\u001b[0m             blocks[i]\u001b[39m.\u001b[39;49mrecompute()\n\u001b[1;32m    212\u001b[0m \u001b[39m# ReducedFunctional can result in a scalar or an assembled 1-form\u001b[39;00m\n\u001b[1;32m    213\u001b[0m func_value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunctional\u001b[39m.\u001b[39mblock_variable\u001b[39m.\u001b[39msaved_output\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/pyadjoint/pyadjoint/block.py:350\u001b[0m, in \u001b[0;36mBlock.recompute\u001b[0;34m(self, markings)\u001b[0m\n\u001b[1;32m    347\u001b[0m prepared \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprepare_recompute_component(inputs, relevant_outputs)\n\u001b[1;32m    349\u001b[0m \u001b[39mfor\u001b[39;00m idx, out \u001b[39min\u001b[39;00m relevant_outputs:\n\u001b[0;32m--> 350\u001b[0m     output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrecompute_component(inputs,\n\u001b[1;32m    351\u001b[0m                                       out,\n\u001b[1;32m    352\u001b[0m                                       idx,\n\u001b[1;32m    353\u001b[0m                                       prepared)\n\u001b[1;32m    354\u001b[0m     \u001b[39mif\u001b[39;00m output \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    355\u001b[0m         out\u001b[39m.\u001b[39mcheckpoint \u001b[39m=\u001b[39m output\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/firedrake/firedrake/adjoint_utils/blocks/solving.py:530\u001b[0m, in \u001b[0;36mGenericSolveBlock.recompute_component\u001b[0;34m(self, inputs, block_variable, idx, prepared)\u001b[0m\n\u001b[1;32m    528\u001b[0m func \u001b[39m=\u001b[39m prepared[\u001b[39m2\u001b[39m]\n\u001b[1;32m    529\u001b[0m bcs \u001b[39m=\u001b[39m prepared[\u001b[39m3\u001b[39m]\n\u001b[0;32m--> 530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward_solve(lhs, rhs, func, bcs)\n\u001b[1;32m    531\u001b[0m \u001b[39mreturn\u001b[39;00m maybe_disk_checkpoint(result)\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/firedrake/firedrake/adjoint_utils/blocks/solving.py:626\u001b[0m, in \u001b[0;36mNonlinearVariationalSolveBlock._forward_solve\u001b[0;34m(self, lhs, rhs, func, bcs, **kwargs)\u001b[0m\n\u001b[1;32m    624\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ad_nlvs_replace_forms()\n\u001b[1;32m    625\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ad_nlvs\u001b[39m.\u001b[39mparameters\u001b[39m.\u001b[39mupdate(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msolver_params)\n\u001b[0;32m--> 626\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_ad_nlvs\u001b[39m.\u001b[39;49msolve()\n\u001b[1;32m    627\u001b[0m func\u001b[39m.\u001b[39massign(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ad_nlvs\u001b[39m.\u001b[39m_problem\u001b[39m.\u001b[39mu)\n\u001b[1;32m    628\u001b[0m \u001b[39mreturn\u001b[39;00m func\n",
-      "File \u001b[0;32mpetsc4py/PETSc/Log.pyx:115\u001b[0m, in \u001b[0;36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpetsc4py/PETSc/Log.pyx:116\u001b[0m, in \u001b[0;36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/firedrake/firedrake/adjoint_utils/variational_solver.py:89\u001b[0m, in \u001b[0;36mNonlinearVariationalSolverMixin._ad_annotate_solve.<locals>.wrapper\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m     tape\u001b[39m.\u001b[39madd_block(block)\n\u001b[1;32m     88\u001b[0m \u001b[39mwith\u001b[39;00m stop_annotating():\n\u001b[0;32m---> 89\u001b[0m     out \u001b[39m=\u001b[39m solve(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     91\u001b[0m \u001b[39mif\u001b[39;00m annotate:\n\u001b[1;32m     92\u001b[0m     block\u001b[39m.\u001b[39madd_output(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ad_problem\u001b[39m.\u001b[39m_ad_u\u001b[39m.\u001b[39mcreate_block_variable())\n",
-      "File \u001b[0;32m~/Workplace/firedrake-2023-09-11/src/firedrake/firedrake/variational_solver.py:279\u001b[0m, in \u001b[0;36mNonlinearVariationalSolver.solve\u001b[0;34m(self, bounds)\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[39mfor\u001b[39;00m ctx \u001b[39min\u001b[39;00m chain((\u001b[39mself\u001b[39m\u001b[39m.\u001b[39minserted_options(), dmhooks\u001b[39m.\u001b[39madd_hooks(dm, \u001b[39mself\u001b[39m, appctx\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ctx)),\n\u001b[1;32m    277\u001b[0m                          \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_transfer_operators):\n\u001b[1;32m    278\u001b[0m             stack\u001b[39m.\u001b[39menter_context(ctx)\n\u001b[0;32m--> 279\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msnes\u001b[39m.\u001b[39;49msolve(\u001b[39mNone\u001b[39;49;00m, work)\n\u001b[1;32m    280\u001b[0m     work\u001b[39m.\u001b[39mcopy(u)\n\u001b[1;32m    281\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setup \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "def callback():\n",
-    "    initial_misfit = assemble(\n",
-    "        (Tic.block_variable.checkpoint.restore() - Tic_ref) ** 2 * dx\n",
-    "    )\n",
-    "    final_misfit = assemble(\n",
-    "        (T.block_variable.checkpoint.restore() - Tobs) ** 2 * dx\n",
-    "    )\n",
-    "\n",
-    "    log(f\"Initial misfit; {initial_misfit}; final misfit: {final_misfit}\")\n",
+   "outputs": [],
+   "source": [
+    "class callback_class():\n",
+    "    def __init__(self):\n",
+    "        self.fi = File(\"Solution.pvd\")\n",
+    "    def __call__(self):\n",
+    "        self.fi.write(Tic.block_variable.checkpoint)\n",
+    "\n",
+    "        initial_misfit = assemble(\n",
+    "            (Tic.block_variable.checkpoint - Tic_ref) ** 2 * dx\n",
+    "        )\n",
+    "        final_misfit = assemble(\n",
+    "            (T.block_variable.checkpoint - Tobs) ** 2 * dx\n",
+    "        )\n",
+    "\n",
+    "        log(f\"Initial misfit; {initial_misfit}; final misfit: {final_misfit}\")\n",
+    "# initialising callback\n",
+    "callback = callback_class()\n",
     "\n",
     "# Perform a bounded nonlinear optimisation where temperature\n",
     "# is only permitted to lie in the range [0, 1]\n",
@@ -726,15 +683,62 @@
     "T_lb.assign(0.0)\n",
     "T_ub.assign(1.0)\n",
     "\n",
-    "minimisation_problem = MinimizationProblem(reduced_functional, bounds=(T_lb, T_ub))\n",
-    "\n",
+    "minimisation_problem = MinimizationProblem(reduced_functional, bounds=(T_lb, T_ub))\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c92f5fb4",
+   "metadata": {},
+   "source": [
+    "And finally we run the optimisation for 10 iterations. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2eb9c68c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "minimisation_parameters['Step']['Trust Region']['Lin-More']['Cauchy Point']['Normalize Initial Step Size'] = True\n",
+    "minimisation_parameters['Step']['Trust Region']['Lin-More']['Cauchy Point']['Initial Step Size'] = 1.63e-1\n",
+    "minimisation_parameters['Status Test']['Iteration Limit'] = 10\n",
     "optimiser = LinMoreOptimiser(\n",
     "    minimisation_problem,\n",
     "    minimisation_parameters,\n",
+    "    auto_checkpoint=False,\n",
     ")\n",
     "optimiser.add_callback(callback)\n",
-    "optimiser.run()\n"
+    "optimiser.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e9ca3c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solution = optimiser.rol_solver.rolvector.dat[0]\n",
+    "solution.rename(\"solution\")\n",
+    "# Let's visualise the final temperature field\n",
+    "File(\"solution.pvd\").write(solution)\n",
+    "import pyvista as pv\n",
+    "temp_data = pv.read(\"temp_0.vtu\")\n",
+    "plotter = pv.Plotter(notebook=True)\n",
+    "plotter.add_mesh(temp_data, cmap='bwr', clim=[0, 1],scalar_bar_args={'title': 'Solution', 'position_x': 0.2, 'position_y': 0.85})\n",
+    "plotter.camera_position = \"xy\"\n",
+    "\n",
+    "plotter.show(jupyter_backend=\"static\", interactive=False)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8918c21",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -753,7 +757,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4 (main, Jun 20 2023, 17:23:00) [Clang 14.0.3 (clang-1403.0.22.14.1)]"
+   "version": "3.11.4"
   },
   "vscode": {
    "interpreter": {
diff --git a/11-GD-2D-cylindrical.ipynb b/11-GD-2D-cylindrical.ipynb
deleted file mode 100644
index 0467658..0000000
--- a/11-GD-2D-cylindrical.ipynb
+++ /dev/null
@@ -1,454 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Adjoint-based Optimization for an Annulus Domain with Viscoplastic Rheology\n",
-    "\n",
-    "In this exercise, we extend our understanding of adjoint-based optimization to a more complex geophysical scenario. Unlike the previous exercise that focused on a simple square domain, here we explore an annulus domain, mimicking Earth's mantle. We also introduce a more sophisticated viscoplastic rheology model. \n",
-    "\n",
-    "Key differences from the previous exercise:\n",
-    "1. **Geometry**: We are working with an annulus domain.\n",
-    "2. **Rheology**: We introduce a viscoplastic model.\n",
-    "\n",
-    "Let's dive in! But before that, let's make sure we have all the dependencies. \n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we first import the required libraries. We then get the tape from `pyadjoint` using `get_working_tape()` and clear it using `tape.clear_tape()`. This ensures that the tape is clean before we start, which is particularly important when running multiple instances of adjoint simulations.\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we set up the geometry of our annulus domain. We define $r_{max}=2.2$, $r_{min}=1.2$. This ensures a unit length for the thickness of the domain, while mainating the same ratio between surface and CMB as it is for the Earth. We also obtain the non-dimensional radius of 410 and 660 discontinuities in this email, which will be used to implement the depth dependence of  km depths, which represent viscosity jumps.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up geometry:\n",
-    " rmax = 2.22\n",
-    " rmax_earth = 6370  # Radius of Earth [km]\n",
-    " rmin_earth = rmax_earth - 2900  # Radius of CMB [km]\n",
-    " r_410_earth = rmax_earth - 410  # 410 radius [km]\n",
-    " r_660_earth = rmax_earth - 660  # 660 raidus [km]\n",
-    " r_410 = rmax - (rmax_earth - r_410_earth) / (rmax_earth - rmin_earth)\n",
-    " r_660 = rmax - (rmax_earth - r_660_earth) / (rmax_earth - rmin_earth)\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We load the mesh from our reference simulation stored in `Checkpoint230.h5`. After that, we enable disk checkpointing for adjoint intermediary fields. This is crucial for managing memory efficiently, especially for large problems.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with CheckpointFile(\"Checkpoint230.h5\", \"r\") as f:\n",
-    "        mesh = f.load_mesh(\"firedrake_default_extruded\")\n",
-    "\n",
-    "    enable_disk_checkpointing()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We define various function spaces for our problem. Importantly, we use \\( Q2 \\) elements for temperature but \\( Q1 \\) elements for our control variable. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up function spaces for the Q2Q1 pair\n",
-    "V = VectorFunctionSpace(mesh, \"CG\", 2)  # Velocity function space (vector)\n",
-    "W = FunctionSpace(mesh, \"CG\", 1)  # Pressure function space (scalar)\n",
-    "Q = FunctionSpace(mesh, \"CG\", 2)  # Temperature function space (scalar)\n",
-    "Q1 = FunctionSpace(mesh, \"CG\", 1)  # Control function space\n",
-    "Z = MixedFunctionSpace([V, W])  # Mixed function space\n",
-    "\n",
-    "# Test functions and functions to hold solutions:\n",
-    "z = Function(Z)  # A field over the mixed function space Z\n",
-    "u, p = split(z)  # Symbolic UFL expressions for u and p\n",
-    "\n",
-    "X = SpatialCoordinate(mesh)\n",
-    "r = sqrt(X[0] ** 2 + X[1] ** 2)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We set up the Rayleigh number and the Boussinesq Approximation here. We also define the time-stepping parameters and load our control variable $\\text{Tic}$ and $\\text{Taverage}$ for regularisation.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Running cells requires notebook package.\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
-     ]
-    }
-   ],
-   "source": [
-    "Ra = Constant(1e7)  # Rayleigh number\n",
-    "approximation = BoussinesqApproximation(Ra)\n",
-    "\n",
-    "# Define time stepping parameters:\n",
-    "max_timesteps = 200\n",
-    "delta_t = Constant(5e-6)  # Constant time step\n",
-    "\n",
-    "# Without a restart to continue from, our initial guess is the final state of the forward run\n",
-    "# We need to project the state from Q2 into Q1\n",
-    "Tic = Function(Q1, name=\"Initial Temperature\")\n",
-    "Taverage = Function(Q1, name=\"Average Temperature\")\n",
-    "\n",
-    "checkpoint_file = CheckpointFile(\"Checkpoint_State.h5\", \"r\")\n",
-    "# Initialise the control\n",
-    "Tic.project(\n",
-    "    checkpoint_file.load_function(mesh, \"Temperature\", idx=max_timesteps - 1)\n",
-    ")\n",
-    "Taverage.project(checkpoint_file.load_function(mesh, \"Average Temperature\", idx=0))\n",
-    "\n",
-    "# Temperature function in Q2, where we solve the equations\n",
-    "T = Function(Q, name=\"Temperature\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we build a complex viscoplastic rheology model. We use a step function to represent the viscosity jumps at 410 and 660 km depths. The rheology has two components: a linear part that is temperature-dependent and a plastic part that is rheology-dependent.\n",
-    "\n",
-    "\\[\n",
-    "\\text{Equations for viscosity here}\n",
-    "\\]\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we set up the solvers for the Stokes and energy equations. Doing that we also set up nullspaces and near-nullspaces for efficient solving of the system."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Nullspaces and near-nullspaces:\n",
-    "Z_nullspace = create_stokes_nullspace(Z, closed=True, rotational=True)\n",
-    "Z_near_nullspace = create_stokes_nullspace(\n",
-    "    Z, closed=False, rotational=True, translations=[0, 1]\n",
-    ")\n",
-    "\n",
-    "stokes_bcs = {\n",
-    "    \"top\": {\"un\": 0},\n",
-    "    \"bottom\": {\"un\": 0},\n",
-    "}\n",
-    "temp_bcs = {\n",
-    "    \"top\": {\"T\": 0.0},\n",
-    "    \"bottom\": {\"T\": 1.0},\n",
-    "}\n",
-    "\n",
-    "energy_solver = EnergySolver(\n",
-    "    T,\n",
-    "    u,\n",
-    "    approximation,\n",
-    "    delta_t,\n",
-    "    ImplicitMidpoint,\n",
-    "    bcs=temp_bcs,\n",
-    ")\n",
-    "\n",
-    "stokes_solver = StokesSolver(\n",
-    "    z,\n",
-    "    T,\n",
-    "    approximation,\n",
-    "    mu=mu,\n",
-    "    bcs=stokes_bcs,\n",
-    "    cartesian=False,\n",
-    "    nullspace=Z_nullspace,\n",
-    "    transpose_nullspace=Z_nullspace,\n",
-    "    near_nullspace=Z_near_nullspace,\n",
-    "    solver_parameters=newton_stokes_solver_parameters,\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The control variable for our optimisation problem is set to be `Tic`, which represents the initial condition for temperature. This is the parameter we aim to optimise in our inverse problem.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Control variable for optimisation\n",
-    "control = Control(Tic)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We initialize `u_misfit` to zero. This variable will be used to accumulate the misfit between the observed and simulated surface velocity as we proceed through the time steps.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "u_misfit = 0.0"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we need to project the initial condition `Tic` from the $Q1$ space to $Q2$ space. This is necessary because the temperature is solved in $Q2$ space. We also impose boundary conditions while doing this projection.\n",
-    "After the projection, we are ready to run the forward simulation to populate the tape, which will be used for adjoint computations later. For each time step, we solve both the Stokes and energy equations. We then update the variable `u_misfit` to accumulate the surface velocity misfit using the observed values. While running the forward problem, we also load reference velocity fields information that will be used in the objective function:\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T.project(Tic, bcs=energy_solver.strong_bcs)\n",
-    "for timestep in range(0, max_timesteps):\n",
-    "    stokes_solver.solve()\n",
-    "    energy_solver.solve()\n",
-    "    uobs = checkpoint_file.load_function(mesh, name=\"Velocity\", idx=timestep)\n",
-    "    u_misfit += assemble(dot(u - uobs, u - uobs) * ds_t)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We also load various fields\n",
-    "- `Tobs`: The observed final state of temperature.\n",
-    "- `Tic_ref`: The reference initial state of temperature, used to measure the performance of an inverse scheme.\n",
-    "- `Taverage`: The average temperature profile, used for regularization.\n",
-    "\n",
-    "We then close the checkpoint file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the observed final state\n",
-    "Tobs = checkpoint_file.load_function(mesh, \"Temperature\", idx=max_timesteps - 1)\n",
-    "Tobs.rename(\"Observed Temperature\")\n",
-    "\n",
-    "# Load the reference initial state\n",
-    "# Needed to measure performance of weightings\n",
-    "Tic_ref = checkpoint_file.load_function(mesh, \"Temperature\", idx=0)\n",
-    "Tic_ref.rename(\"Reference Initial Temperature\")\n",
-    "\n",
-    "# Load the average temperature profile\n",
-    "Taverage = checkpoint_file.load_function(mesh, \"Average Temperature\", idx=0)\n",
-    "\n",
-    "checkpoint_file.close()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We define the components of the objective functional, which include terms for damping, smoothing, and the misfits for temperature and surface velocity. These are combined to form the overall objective function that we aim to minimize."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the component terms of the overall objective functional\n",
-    "damping = assemble((Tic - Taverage) ** 2 * dx)\n",
-    "norm_damping = assemble(Taverage**2 * dx)\n",
-    "smoothing = assemble(dot(grad(Tic - Taverage), grad(Tic - Taverage)) * dx)\n",
-    "norm_smoothing = assemble(dot(grad(Tobs), grad(Tobs)) * dx)\n",
-    "norm_obs = assemble(Tobs**2 * dx)\n",
-    "norm_u_surface = assemble(dot(uobs, uobs) * ds_t)\n",
-    "\n",
-    "# Temperature misfit between solution and observation\n",
-    "t_misfit = assemble((T - Tobs) ** 2 * dx)\n",
-    "\n",
-    "objective = (\n",
-    "    t_misfit +\n",
-    "    alpha_u * (norm_obs * u_misfit / max_timesteps / norm_u_surface) +\n",
-    "    alpha_d * (norm_obs * damping / norm_damping) +\n",
-    "    alpha_s * (norm_obs * smoothing / norm_smoothing)\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We pause the annotation to the tape as we are done with the forward run. This ensures that no further operations are added to the tape, which will be used for adjoint calculations.\n",
-    "We define the reduced functional, which represents the objective function with respect to the control variable. This will be used for optimization.\n",
-    "We define a callback function to log the initial and final misfits as the optimization progresses. This is useful for monitoring the optimization process.\n",
-    "We set up a bounded nonlinear optimization problem. The temperature is restricted to be within the range [0, 1]. We then define the minimization problem with these bounds.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# All done with the forward run, stop annotating anything else to the tape\n",
-    "pause_annotation()\n",
-    "# Defining the object for pyadjoint\n",
-    "reduced_functional = ReducedFunctional(objective, control)\n",
-    "def callback():\n",
-    "    initial_misfit = assemble(\n",
-    "        (Tic.block_variable.checkpoint.restore() - Tic_ref) ** 2 * dx\n",
-    "    )\n",
-    "    final_misfit = assemble(\n",
-    "        (T.block_variable.checkpoint.restore() - Tobs) ** 2 * dx\n",
-    "    )\n",
-    "\n",
-    "    log(f\"Initial misfit; {initial_misfit}; final misfit: {final_misfit}\")\n",
-    "# Perform a bounded nonlinear optimisation where temperature\n",
-    "# is only permitted to lie in the range [0, 1]\n",
-    "T_lb = Function(Tic.function_space(), name=\"Lower bound temperature\")\n",
-    "T_ub = Function(Tic.function_space(), name=\"Upper bound temperature\")\n",
-    "T_lb.assign(0.0)\n",
-    "T_ub.assign(1.0)\n",
-    "\n",
-    "minimisation_problem = MinimizationProblem(reduced_functional, bounds=(T_lb, T_ub))\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we set up and run the optimizer. We also add the callback function to log the misfits during the optimization.\n",
-    "If multiple successive optimizations are being performed, it's important to resume annotation to the tape for the next run.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimiser = LinMoreOptimiser(\n",
-    "    minimisation_problem,\n",
-    "    minimisation_parameters,\n",
-    "    checkpoint_dir=\"optimisation_checkpoint\",\n",
-    ")\n",
-    "optimiser.add_callback(callback)\n",
-    "optimiser.run()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "In this exercise, we have delved into a more complex geophysical scenario with a focus on an annulus domain and viscoplastic rheology. This adds layers of realism to our inversion problem and gives us a deeper understanding of the complexities involved in geodynamics simulations.\n",
-    "\n",
-    "Key Takeaways:\n",
-    "1. The geometry and rheology significantly impact the inversion process.\n",
-    "2. Sophisticated rheology models can be implemented efficiently using Firedrake and pyadjoint.\n",
-    "\n"
-   ]
-  }
- ],
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