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diff --git a/docs/source/examples/fmu_cosimulation.ipynb b/docs/source/examples/fmu_cosimulation.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": "# FMU Co-Simulation\n\nDemonstration of integrating Functional Mock-up Units (FMU) as PathSim blocks.\n\n## What is an FMU?\n\nThe [Functional Mock-up Interface](https://fmi-standard.org/) (FMI) is an open standard for model exchange and co-simulation. An FMU is a ZIP archive containing:\n- Model equations (compiled binaries)\n- XML description of variables and metadata\n- Optional resources and documentation\n\nPathSim supports **FMI 2.0** and **FMI 3.0** co-simulation FMUs through the [FMPy](https://github.com/CATIA-Systems/FMPy) library.\n\n## The Coupled Clutches Model\n\nThis example uses a **coupled clutches** system, which is a classic benchmark from the FMI standard examples. The system consists of:\n- Multiple rotating inertias\n- Clutches connecting the inertias\n- An input torque driving the system\n\nThe FMU has:\n- **1 input**: Torque applied to the first clutch\n- **4 outputs**: Angular velocities of the four rotating masses"
+  },
+  {
+   "cell_type": "raw",
+   "id": "cell-1",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "This example demonstrates the :class:`.CoSimulationFMU` block, which wraps an FMU for integration with PathSim's simulation environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Import and Setup\n",
+    "\n",
+    "Note that FMPy must be installed to use FMU blocks:\n",
+    "\n",
+    "```bash\n",
+    "pip install fmpy\n",
+    "```\n",
+    "\n",
+    "First, let's import the required classes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cell-3",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Apply PathSim docs matplotlib style\n",
+    "plt.style.use('../pathsim_docs.mplstyle')\n",
+    "\n",
+    "from pathsim import Simulation, Connection\n",
+    "from pathsim.blocks import Source, Scope\nfrom pathsim_fmi import CoSimulationFMU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-4",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## FMU Path\n",
+    "\n",
+    "We need to provide the path to the FMU file. In this example, we use the coupled clutches FMU:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "cell-5",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import platform\n",
+    "from pathlib import Path\n",
+    "\n",
+    "notebook_dir = Path().resolve()\n",
+    "system = platform.system()\n",
+    "\n",
+    "# Select FMU based on platform\n",
+    "if system == \"Windows\":\n",
+    "    fmu_filename = \"CoupledClutches_CS_win64.fmu\"\n",
+    "elif system == \"Linux\":\n",
+    "    fmu_filename = \"CoupledClutches_CS_linux64.fmu\"\n",
+    "\n",
+    "fmu_path = notebook_dir / \"data\" / fmu_filename\n",
+    "\n",
+    "# Verify FMU exists\n",
+    "if not fmu_path.exists():\n",
+    "    raise FileNotFoundError(f\"FMU file not found at {fmu_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Create the FMU Block"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "cell-7",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The :class:`.CoSimulationFMU` block handles all the FMI protocol details:\n",
+    "- Instantiates the FMU\n",
+    "- Manages initialization and termination\n",
+    "- Synchronizes inputs and outputs\n",
+    "- Performs co-simulation steps at the specified ``dt``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cell-8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
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+    "}",
+    "s",
+    "\"",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-9",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## System Setup\n",
+    "\n",
+    "We create a sinusoidal torque input to drive the system and a scope to record all signals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "cell-10",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Input torque - sinusoidal excitation\n",
+    "src = Source(lambda t: 0.1 * np.sin(5*t))\n",
+    "\n",
+    "# Scope to record all signals\n",
+    "sco = Scope(labels=[\"Input Torque\", \"\u03c9\u2081\", \"\u03c9\u2082\", \"\u03c9\u2083\", \"\u03c9\u2084\"])\n",
+    "\n",
+    "blocks = [fmu, src, sco]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-11",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Connections"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "cell-12",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The FMU block behaves like any other PathSim block. We connect:\n",
+    "- The source to the FMU input (torque)\n",
+    "- The FMU outputs (4 angular velocities) to the scope\n",
+    "- The input signal is also recorded for reference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "cell-13",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "connections = [\n",
+    "    Connection(src[0], fmu[0], sco[0]),  # Input torque to FMU and scope\n",
+    "    Connection(fmu[:4], sco[1:5])        # FMU outputs (4 velocities) to scope\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-14",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "The simulation timestep is set smaller than the FMU communication step size. PathSim automatically handles the scheduling of FMU co-simulation steps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "cell-15",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-10-11 11:03:06,028 - INFO - LOGGING (log: True)\n",
+      "2025-10-11 11:03:06,028 - INFO - BLOCK (type: CoSimulationFMU, dynamic: False, events: 1)\n",
+      "2025-10-11 11:03:06,028 - INFO - BLOCK (type: Source, dynamic: False, events: 0)\n",
+      "2025-10-11 11:03:06,029 - INFO - BLOCK (type: Scope, dynamic: False, events: 0)\n",
+      "2025-10-11 11:03:06,029 - INFO - GRAPH (size: 3, alg. depth: 1, loop depth: 0, runtime: 0.071ms)\n",
+      "2025-10-11 11:03:06,031 - INFO - STARTING -> TRANSIENT (Duration: 20.00s)\n",
+      "2025-10-11 11:03:06,031 - INFO - TRANSIENT:   0% | elapsed: 00:00:00 (eta: --:--:--) | 0 steps (N/A steps/s)\n",
+      "2025-10-11 11:03:06,055 - INFO - TRANSIENT:  20% | elapsed: 00:00:00 (eta: --:--:--) | 2001 steps (83596.2 steps/s)\n",
+      "2025-10-11 11:03:06,078 - INFO - TRANSIENT:  40% | elapsed: 00:00:00 (eta: --:--:--) | 4001 steps (84763.4 steps/s)\n",
+      "2025-10-11 11:03:06,102 - INFO - TRANSIENT:  60% | elapsed: 00:00:00 (eta: --:--:--) | 6000 steps (84356.3 steps/s)\n",
+      "2025-10-11 11:03:06,125 - INFO - TRANSIENT:  80% | elapsed: 00:00:00 (eta: --:--:--) | 8000 steps (86421.1 steps/s)\n",
+      "2025-10-11 11:03:06,146 - INFO - TRANSIENT: 100% | elapsed: 00:00:00 (eta: 00:00:00) | 10001 steps (93754.0 steps/s)\n",
+      "2025-10-11 11:03:06,147 - INFO - TRANSIENT: 100% | elapsed: 00:00:00 (eta: 00:00:00) | 10001 steps (85916.8 avg steps/s)\n",
+      "2025-10-11 11:03:06,147 - INFO - FINISHED -> TRANSIENT (total steps: 10001, successful: 10001, runtime: 116.40 ms)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'total_steps': 10001,\n",
+       " 'successful_steps': 10001,\n",
+       " 'runtime_ms': 116.40140000963584}"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Initialize simulation\n",
+    "# Use a smaller dt than FMU step size for smoother PathSim integration\n",
+    "sim = Simulation(\n",
+    "    blocks=blocks,\n",
+    "    connections=connections,\n",
+    "    dt=fmu.dt/5,  # PathSim timestep is 5x smaller than FMU step\n",
+    "    log=True\n",
+    ")\n",
+    "\n",
+    "# Run simulation for 20 seconds\n",
+    "sim.run(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-16",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Results\n",
+    "\n",
+    "Let's plot the input torque and the angular velocities of all four clutches:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "cell-17",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sco.plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-18",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Analysis: Phase Space\n",
+    "\n",
+    "We can examine the relationship between the angular velocities in phase space:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "cell-19",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get simulation data\n",
+    "time, [torque, w1, w2, w3, w4] = sco.read()\n",
+    "\n",
+    "# Phase portrait\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(9, 4), dpi=200)\n",
+    "\n",
+    "# \u03c9\u2081 vs \u03c9\u2082\n",
+    "axes[0].plot(w1, w2, alpha=0.85)\n",
+    "axes[0].set_xlabel('\u03c9\u2081 [rad/s]')\n",
+    "axes[0].set_ylabel('\u03c9\u2082 [rad/s]')\n",
+    "axes[0].set_title('Phase Portrait: First vs Second Clutch')\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# \u03c9\u2083 vs \u03c9\u2084\n",
+    "axes[1].plot(w3, w4, alpha=0.85)\n",
+    "axes[1].set_xlabel('\u03c9\u2083 [rad/s]')\n",
+    "axes[1].set_ylabel('\u03c9\u2084 [rad/s]')\n",
+    "axes[1].set_title('Phase Portrait: Third vs Fourth Clutch')\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-20",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## FMU Integration Details\n",
+    "\n",
+    "### Communication Step Size\n",
+    "\n",
+    "The FMU performs internal integration steps with its own solver. The `dt` parameter specifies how often PathSim exchanges data with the FMU:\n",
+    "\n",
+    "- **Smaller `dt`**: More frequent data exchange, higher accuracy, slower simulation\n",
+    "- **Larger `dt`**: Less frequent exchange, faster simulation, potentially lower accuracy\n",
+    "\n",
+    "### PathSim vs FMU Timestep\n",
+    "\n",
+    "PathSim can use a different (typically smaller) timestep than the FMU communication interval. This allows:\n",
+    "- Smooth integration with other PathSim blocks\n",
+    "- Fine-grained logging and event detection\n",
+    "- The FMU only performs co-simulation steps at its specified `dt`\n",
+    "\n",
+    "### Event Handling\n",
+    "\n",
+    "The FMU block uses PathSim's event scheduling system to trigger co-simulation steps at regular intervals. You can see the scheduled events:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "cell-21",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total FMU steps: 2001\n",
+      "Expected steps: 2001\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of FMU co-simulation steps performed\n",
+    "print(f\"Total FMU steps: {len(fmu.events[0])}\")\n",
+    "print(f\"Expected steps: {int(20/fmu.dt) + 1}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-22",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Key Features of CoSimulationFMU\n",
+    "\n",
+    "- Supports FMI 2.0 and FMI 3.0 standards\n",
+    "- Automatic metadata extraction and port configuration\n",
+    "- Configurable communication step size\n",
+    "- Event-based synchronization with PathSim\n",
+    "- Full reset and re-initialization support\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/source/examples/fmu_model_exchange_bouncing_ball.ipynb b/docs/source/examples/fmu_model_exchange_bouncing_ball.ipynb
new file mode 100644
index 0000000..b22af95
--- /dev/null
+++ b/docs/source/examples/fmu_model_exchange_bouncing_ball.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _ref-fmu-model-exchange-bouncing-ball:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# FMU ME: Bouncing Ball\n",
+    "\n",
+    "This example demonstrates **Model Exchange FMU** integration with PathSim. Unlike co-simulation FMUs, Model Exchange FMUs provide only the differential equations. PathSim's solvers perform the numerical integration and event detection.\n",
+    "\n",
+    "You can also find the FMU integration tests in the [GitHub repository](https://github.com/milanofthe/pathsim/blob/master/tests/evals/).\n",
+    "\n",
+    "The bouncing ball combines continuous dynamics with discrete events:\n",
+    "\n",
+    "$$\\frac{dh}{dt} = v$$\n",
+    "$$\\frac{dv}{dt} = g$$\n",
+    "\n",
+    "where $h$ is height, $v$ is velocity, and $g = -9.81\\,\\text{m/s}^2$. At impact ($h = 0$), velocity reverses with energy loss: $v^+ = -e \\cdot v^-$ where $e \\in [0,1]$ is the restitution coefficient."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    "This example demonstrates the :class:`.ModelExchangeFMU` block, which provides PathSim with continuous states and derivatives from an FMU. PathSim's solvers handle integration, event detection, and error control."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import and Setup\n",
+    "\n",
+    "Note that FMPy must be installed to use FMU blocks:\n",
+    "\n",
+    "```bash\n",
+    "pip install fmpy\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Apply PathSim docs matplotlib style for consistent, theme-friendly figures\n",
+    "plt.style.use('../pathsim_docs.mplstyle')\n",
+    "\n",
+    "from pathlib import Path\n",
+    "from pathsim import Simulation, Connection\n",
+    "from pathsim.blocks import Scope\nfrom pathsim_fmi import ModelExchangeFMU\n",
+    "from pathsim.solvers import RKBS32"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## FMU Path\n",
+    "\n",
+    "The FMU contains binaries for multiple platforms (Windows, Linux, macOS):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "notebook_dir = Path().resolve()\n",
+    "fmu_path = notebook_dir / \"data\" / \"BouncingBall_ME.fmu\"\n",
+    "\n",
+    "# Verify FMU exists\n",
+    "if not fmu_path.exists():\n",
+    "    raise FileNotFoundError(f\"FMU file not found at {fmu_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## System Definition"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    "The :class:`.ModelExchangeFMU` block exposes continuous states $(h, v)$ and provides derivatives $(\\dot{h}, \\dot{v})$ to PathSim's solvers. Event indicators signal zero-crossings for accurate bounce detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the Model Exchange FMU block\n",
+    "fmu = ModelExchangeFMU(\n",
+    "    fmu_path=str(fmu_path),\n",
+    "    instance_name=\"bouncing_ball\",\n",
+    "    start_values={\"e\": 0.7},  # coefficient of restitution\n",
+    "    tolerance=1e-10,\n",
+    "    verbose=False\n",
+    ")\n",
+    "\n",
+    "# Scope to record height and velocity\n",
+    "sco = Scope(labels=[\"h [m]\", \"v [m/s]\"])\n",
+    "\n",
+    "blocks = [fmu, sco]\n",
+    "\n",
+    "# Connect FMU outputs to scope\n",
+    "connections = [\n",
+    "    Connection(fmu[0], sco[0]),  # height\n",
+    "    Connection(fmu[1], sco[1]),  # velocity\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display FMU metadata:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#",
+    " ",
+    "D",
+    "i",
+    "s",
+    "p",
+    "l",
+    "a",
+    "y",
+    " ",
+    "F",
+    "M",
+    "U",
+    " ",
+    "m",
+    "e",
+    "t",
+    "a",
+    "d",
+    "a",
+    "t",
+    "a",
+    " ",
+    "(",
+    "a",
+    "c",
+    "c",
+    "e",
+    "s",
+    "s",
+    "e",
+    "d",
+    " ",
+    "v",
+    "i",
+    "a",
+    " ",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ")",
+    "\n",
+    "m",
+    "d",
+    " ",
+    "=",
+    " ",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "m",
+    "o",
+    "d",
+    "e",
+    "l",
+    "_",
+    "d",
+    "e",
+    "s",
+    "c",
+    "r",
+    "i",
+    "p",
+    "t",
+    "i",
+    "o",
+    "n",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "M",
+    "o",
+    "d",
+    "e",
+    "l",
+    " ",
+    "N",
+    "a",
+    "m",
+    "e",
+    ":",
+    " ",
+    "{",
+    "m",
+    "d",
+    ".",
+    "m",
+    "o",
+    "d",
+    "e",
+    "l",
+    "N",
+    "a",
+    "m",
+    "e",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "F",
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+    " ",
+    "V",
+    "e",
+    "r",
+    "s",
+    "i",
+    "o",
+    "n",
+    ":",
+    " ",
+    "{",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "f",
+    "m",
+    "i",
+    "_",
+    "v",
+    "e",
+    "r",
+    "s",
+    "i",
+    "o",
+    "n",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "D",
+    "e",
+    "s",
+    "c",
+    "r",
+    "i",
+    "p",
+    "t",
+    "i",
+    "o",
+    "n",
+    ":",
+    " ",
+    "{",
+    "m",
+    "d",
+    ".",
+    "d",
+    "e",
+    "s",
+    "c",
+    "r",
+    "i",
+    "p",
+    "t",
+    "i",
+    "o",
+    "n",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "G",
+    "e",
+    "n",
+    "e",
+    "r",
+    "a",
+    "t",
+    "i",
+    "o",
+    "n",
+    " ",
+    "T",
+    "o",
+    "o",
+    "l",
+    ":",
+    " ",
+    "{",
+    "m",
+    "d",
+    ".",
+    "g",
+    "e",
+    "n",
+    "e",
+    "r",
+    "a",
+    "t",
+    "i",
+    "o",
+    "n",
+    "T",
+    "o",
+    "o",
+    "l",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "\\",
+    "n",
+    "C",
+    "o",
+    "n",
+    "t",
+    "i",
+    "n",
+    "u",
+    "o",
+    "u",
+    "s",
+    " ",
+    "s",
+    "t",
+    "a",
+    "t",
+    "e",
+    "s",
+    ":",
+    " ",
+    "{",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "n",
+    "_",
+    "s",
+    "t",
+    "a",
+    "t",
+    "e",
+    "s",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "E",
+    "v",
+    "e",
+    "n",
+    "t",
+    " ",
+    "i",
+    "n",
+    "d",
+    "i",
+    "c",
+    "a",
+    "t",
+    "o",
+    "r",
+    "s",
+    ":",
+    " ",
+    "{",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "n",
+    "_",
+    "e",
+    "v",
+    "e",
+    "n",
+    "t",
+    "_",
+    "i",
+    "n",
+    "d",
+    "i",
+    "c",
+    "a",
+    "t",
+    "o",
+    "r",
+    "s",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "O",
+    "u",
+    "t",
+    "p",
+    "u",
+    "t",
+    "s",
+    ":",
+    " ",
+    "{",
+    "l",
+    "e",
+    "n",
+    "(",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "o",
+    "u",
+    "t",
+    "p",
+    "u",
+    "t",
+    "_",
+    "r",
+    "e",
+    "f",
+    "s",
+    ")",
+    "}",
+    "\"",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation Setup"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    "PathSim integrates the FMU using an adaptive Runge-Kutta solver (:class:`.RKBS32`) with error control. The solver automatically adjusts timesteps for accurate event detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize simulation\n",
+    "sim = Simulation(\n",
+    "    blocks,\n",
+    "    connections,\n",
+    "    dt=0.01,\n",
+    "    dt_max=0.01,\n",
+    "    Solver=RKBS32,\n",
+    "    tolerance_lte_rel=1e-6,\n",
+    "    tolerance_lte_abs=1e-9,\n",
+    "    log=True\n",
+    ")\n",
+    "\n",
+    "# Run simulation\n",
+    "sim.run(4.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results\n",
+    "\n",
+    "Plot the trajectory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sco.plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Event Visualization\n",
+    "\n",
+    "Mark the detected bounce events:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time, (h, v) = sco.read()\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(9, 6), sharex=True, dpi=130)\n",
+    "\n",
+    "# Mark bounce events\n",
+    "for ax in axes:\n",
+    "    for t in fmu.events[0]:\n",
+    "        ax.axvline(t, ls=\"--\", lw=1, c=\"gray\", \n",
+    "                   label=\"Bounce Event\" if t == list(fmu.events[0])[0] else None)\n",
+    "\n",
+    "axes[0].plot(time, h, lw=2)\n",
+    "axes[0].set_ylabel(\"Height [m]\")\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "axes[1].plot(time, v, lw=2, color='orange')\n",
+    "axes[1].set_xlabel(\"Time [s]\")\n",
+    "axes[1].set_ylabel(\"Velocity [m/s]\")\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/docs/source/examples/fmu_model_exchange_vanderpol.ipynb b/docs/source/examples/fmu_model_exchange_vanderpol.ipynb
new file mode 100644
index 0000000..8ca840e
--- /dev/null
+++ b/docs/source/examples/fmu_model_exchange_vanderpol.ipynb
@@ -0,0 +1,770 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    ".. _ref-fmu-model-exchange-vanderpol:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# FMU ME: Van der Pol\n",
+    "\n",
+    "This example demonstrates **Model Exchange FMU** integration with a nonlinear oscillator. The Van der Pol equation exhibits self-sustained oscillations:\n",
+    "\n",
+    "\n",
+    "$$\\ddot{x} - \\mu(1 - x^2)\\dot{x} + x = 0$$\n",
+    "\n",
+    "\n",
+    "where $\\mu > 0$ controls nonlinearity. As a first-order system:\n",
+    "\n",
+    "$$\\frac{dx_0}{dt} = x_1$$\n",
+    "$$\\frac{dx_1}{dt} = \\mu(1 - x_0^2)x_1 - x_0$$\n",
+    "\n",
+    "You can also find the FMU integration tests in the [GitHub repository](https://github.com/milanofthe/pathsim/blob/master/tests/evals/)."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "This example demonstrates the :class:`.ModelExchangeFMU` block with purely continuous-time dynamics (no events), showcasing PathSim's adaptive integration for nonlinear systems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Apply PathSim docs matplotlib style for consistent, theme-friendly figures\n",
+    "plt.style.use('../pathsim_docs.mplstyle')\n",
+    "\n",
+    "from pathlib import Path\n",
+    "from pathsim import Simulation, Connection\n",
+    "from pathsim.blocks import Scope\nfrom pathsim_fmi import ModelExchangeFMU\n",
+    "from pathsim.solvers import RKDP54, ESDIRK43"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## FMU Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "notebook_dir = Path().resolve()\n",
+    "fmu_path = notebook_dir / \"data\" / \"VanDerPol_ME.fmu\"\n",
+    "\n",
+    "# Verify FMU exists\n",
+    "if not fmu_path.exists():\n",
+    "    raise FileNotFoundError(f\"FMU file not found at {fmu_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## System Definition\n",
+    "\n",
+    "We simulate with $\\mu = 1.0$ for clear nonlinear oscillations:"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    "The :class:`.ModelExchangeFMU` exposes states $(x_0, x_1)$ and provides derivatives to PathSim's solvers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Model Exchange FMU\n",
+    "fmu = ModelExchangeFMU(\n",
+    "    fmu_path=str(fmu_path),\n",
+    "    instance_name=\"vanderpol\",\n",
+    "    start_values={\n",
+    "        \"mu\": 1.0,     # nonlinearity parameter\n",
+    "        \"x0\": 2.0,     # initial position\n",
+    "        \"x1\": 0.0,     # initial velocity\n",
+    "    },\n",
+    "    tolerance=1e-8,\n",
+    "    verbose=False\n",
+    ")\n",
+    "\n",
+    "# Scope to record states\n",
+    "sco = Scope(labels=[r\"$x_0$\", r\"$x_1$\"])\n",
+    "\n",
+    "blocks = [fmu, sco]\n",
+    "\n",
+    "# Connections\n",
+    "connections = [\n",
+    "    Connection(fmu[0], sco[0]),\n",
+    "    Connection(fmu[1], sco[1]),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display FMU metadata:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#",
+    " ",
+    "D",
+    "i",
+    "s",
+    "p",
+    "l",
+    "a",
+    "y",
+    " ",
+    "F",
+    "M",
+    "U",
+    " ",
+    "m",
+    "e",
+    "t",
+    "a",
+    "d",
+    "a",
+    "t",
+    "a",
+    " ",
+    "(",
+    "a",
+    "c",
+    "c",
+    "e",
+    "s",
+    "s",
+    "e",
+    "d",
+    " ",
+    "v",
+    "i",
+    "a",
+    " ",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ")",
+    "\n",
+    "m",
+    "d",
+    " ",
+    "=",
+    " ",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "m",
+    "o",
+    "d",
+    "e",
+    "l",
+    "_",
+    "d",
+    "e",
+    "s",
+    "c",
+    "r",
+    "i",
+    "p",
+    "t",
+    "i",
+    "o",
+    "n",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "M",
+    "o",
+    "d",
+    "e",
+    "l",
+    " ",
+    "N",
+    "a",
+    "m",
+    "e",
+    ":",
+    " ",
+    "{",
+    "m",
+    "d",
+    ".",
+    "m",
+    "o",
+    "d",
+    "e",
+    "l",
+    "N",
+    "a",
+    "m",
+    "e",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "F",
+    "M",
+    "I",
+    " ",
+    "V",
+    "e",
+    "r",
+    "s",
+    "i",
+    "o",
+    "n",
+    ":",
+    " ",
+    "{",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "f",
+    "m",
+    "i",
+    "_",
+    "v",
+    "e",
+    "r",
+    "s",
+    "i",
+    "o",
+    "n",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "D",
+    "e",
+    "s",
+    "c",
+    "r",
+    "i",
+    "p",
+    "t",
+    "i",
+    "o",
+    "n",
+    ":",
+    " ",
+    "{",
+    "m",
+    "d",
+    ".",
+    "d",
+    "e",
+    "s",
+    "c",
+    "r",
+    "i",
+    "p",
+    "t",
+    "i",
+    "o",
+    "n",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "G",
+    "e",
+    "n",
+    "e",
+    "r",
+    "a",
+    "t",
+    "i",
+    "o",
+    "n",
+    " ",
+    "T",
+    "o",
+    "o",
+    "l",
+    ":",
+    " ",
+    "{",
+    "m",
+    "d",
+    ".",
+    "g",
+    "e",
+    "n",
+    "e",
+    "r",
+    "a",
+    "t",
+    "i",
+    "o",
+    "n",
+    "T",
+    "o",
+    "o",
+    "l",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "\\",
+    "n",
+    "C",
+    "o",
+    "n",
+    "t",
+    "i",
+    "n",
+    "u",
+    "o",
+    "u",
+    "s",
+    " ",
+    "s",
+    "t",
+    "a",
+    "t",
+    "e",
+    "s",
+    ":",
+    " ",
+    "{",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "n",
+    "_",
+    "s",
+    "t",
+    "a",
+    "t",
+    "e",
+    "s",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "E",
+    "v",
+    "e",
+    "n",
+    "t",
+    " ",
+    "i",
+    "n",
+    "d",
+    "i",
+    "c",
+    "a",
+    "t",
+    "o",
+    "r",
+    "s",
+    ":",
+    " ",
+    "{",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "n",
+    "_",
+    "e",
+    "v",
+    "e",
+    "n",
+    "t",
+    "_",
+    "i",
+    "n",
+    "d",
+    "i",
+    "c",
+    "a",
+    "t",
+    "o",
+    "r",
+    "s",
+    "}",
+    "\"",
+    ")",
+    "\n",
+    "p",
+    "r",
+    "i",
+    "n",
+    "t",
+    "(",
+    "f",
+    "\"",
+    "O",
+    "u",
+    "t",
+    "p",
+    "u",
+    "t",
+    "s",
+    ":",
+    " ",
+    "{",
+    "l",
+    "e",
+    "n",
+    "(",
+    "f",
+    "m",
+    "u",
+    ".",
+    "f",
+    "m",
+    "u",
+    "_",
+    "w",
+    "r",
+    "a",
+    "p",
+    "p",
+    "e",
+    "r",
+    ".",
+    "o",
+    "u",
+    "t",
+    "p",
+    "u",
+    "t",
+    "_",
+    "r",
+    "e",
+    "f",
+    "s",
+    ")",
+    "}",
+    "\"",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation Setup"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "We use a high-order adaptive solver (:class:`.RKDP54`) for accurate nonlinear integration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize simulation\n",
+    "sim = Simulation(\n",
+    "    blocks,\n",
+    "    connections,\n",
+    "    dt=0.1,\n",
+    "    dt_max=0.1,\n",
+    "    Solver=RKDP54,\n",
+    "    tolerance_lte_rel=1e-6,\n",
+    "    tolerance_lte_abs=1e-9,\n",
+    "    log=True\n",
+    ")\n",
+    "\n",
+    "# Run simulation\n",
+    "sim.run(30.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sco.plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Relaxation Oscillations\n",
+    "\n",
+    "For very large $\\mu$, the system exhibits relaxation oscillations with extreme stiffness. We use $\\mu = 500$ to demonstrate PathSim's capability with severe stiffness:"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    "Stiff systems require implicit solvers with large stability regions. We use :class:`.ESDIRK43`, a 4th-order implicit solver designed for stiff problems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fmu_stiff = ModelExchangeFMU(\n",
+    "    fmu_path=str(fmu_path),\n",
+    "    instance_name=\"vdp_stiff\",\n",
+    "    start_values={\"mu\": 500.0, \"x0\": 2.0, \"x1\": 0.0},\n",
+    "    tolerance=1e-8,\n",
+    "    verbose=False\n",
+    ")\n",
+    "\n",
+    "sco_stiff = Scope(labels=[r\"$x_0$\", r\"$x_1$\"])\n",
+    "\n",
+    "sim_stiff = Simulation(\n",
+    "    [fmu_stiff, sco_stiff],\n",
+    "    [Connection(fmu_stiff[0], sco_stiff[0]), Connection(fmu_stiff[1], sco_stiff[1])],\n",
+    "    dt=0.1,\n",
+    "    Solver=ESDIRK43,          # implicit solver for stiff systems\n",
+    "    tolerance_lte_rel=1e-4,\n",
+    "    tolerance_lte_abs=1e-6,\n",
+    "    tolerance_fpi=1e-8,\n",
+    "    log=True\n",
+    ")\n",
+    "\n",
+    "sim_stiff.run(1500.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_stiff, (x0_stiff, x1_stiff) = sco_stiff.read()\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(10, 6), sharex=True, dpi=130)\n",
+    "\n",
+    "axes[0].plot(time_stiff, x0_stiff, lw=2)\n",
+    "axes[0].set_ylabel(r'$x_0$')\n",
+    "axes[0].set_title(r'Relaxation Oscillations ($\\mu = 500$)')\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "axes[1].plot(time_stiff, x1_stiff, lw=2, color='orange')\n",
+    "axes[1].set_xlabel('Time [s]')\n",
+    "axes[1].set_ylabel(r'$x_1$')\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Features\n",
+    "\n",
+    "- Seamless integration of nonlinear dynamics\n",
+    "- Adaptive timestepping for varying dynamics  \n",
+    "- Parameter sweep capabilities\n",
+    "- Handles moderately stiff systems"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/docs/source/examples/linear_feedback.ipynb b/docs/source/examples/linear_feedback.ipynb
deleted file mode 100644
index f7684c0..0000000
--- a/docs/source/examples/linear_feedback.ipynb
+++ /dev/null
@@ -1,212 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0",
-   "metadata": {},
-   "source": "# Linear Feedback System\n\nSimulation of a simple linear feedback system with step response.\n\nYou can also find this example as a single file in the [GitHub repository](https://github.com/milanofthe/pathsim/blob/master/examples/example_feedback.py)."
-  },
-  {
-   "cell_type": "raw",
-   "id": "1",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
-   "source": [
-    ".. image:: figures/figures_g/linear_feedback_blockdiagram_g.png\n",
-    "   :width: 700\n",
-    "   :align: center\n",
-    "   :alt: block diagram of linear feedback system"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "2",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
-   "source": [
-    "The block diagramm above can be translated to a netlist by using the blocks and the connection class provided by PathSim. First lets import the :class:`.Simulation` and :class:`.Connection` classes and the required blocks from the block library:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Apply PathSim docs matplotlib style for consistent, theme-friendly figures\n",
-    "plt.style.use('../pathsim_docs.mplstyle')\n",
-    "\n",
-    "from pathsim import Simulation, Connection\n",
-    "from pathsim.blocks import Source, Integrator, Amplifier, Adder, Scope"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4",
-   "metadata": {},
-   "source": [
-    "Then lets define the system parameters such as the initial value `x0` of the integrator, the linear feedback gain `a` and the time dependend source function `s(t)`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# System parameters\n",
-    "a, x0 = -1, 2\n",
-    "\n",
-    "# Delay for step function\n",
-    "tau = 3\n",
-    "\n",
-    "# Step function\n",
-    "def s(t):\n",
-    "    return int(t>tau)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6",
-   "metadata": {},
-   "source": [
-    "Now we can construct the system by instantiating the blocks we need with their corresponding prameters and collect them together in a list:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Blocks that define the system\n",
-    "Src = Source(s)\n",
-    "Int = Integrator(x0)\n",
-    "Amp = Amplifier(a)\n",
-    "Add = Adder()\n",
-    "Sco = Scope(labels=[\"step\", \"response\"])\n",
-    "\n",
-    "blocks = [Src, Int, Amp, Add, Sco]"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "8",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
-   "source": [
-    "Afterwards, the connections between the blocks can be defined. The first argument of the :class:`.Connection` class is the source block and its port (``Src[0]`` would be port ``0`` of the instance of the :class:`.Source` block, which is also the default port)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The connections between the blocks\n",
-    "connections = [\n",
-    "    Connection(Src, Add[0], Sco[0]),\n",
-    "    Connection(Amp, Add[1]),\n",
-    "    Connection(Add, Int),\n",
-    "    Connection(Int, Amp, Sco[1])\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "10",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
-   "source": [
-    "Finally we can instantiate the ``Simulation`` with the blocks, connections and some additional parameters such as the timestep. In this case, no special ODE solver is specified, so PathSim uses the default :class:`.SSPRK22` integrator which is a fixed step 2nd order explicit Runge-Kutta method. A good starting point. Then we can run the simulation for some duration which is set as ``4*tau`` in this example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize simulation with the blocks, connections, timestep\n",
-    "Sim = Simulation(blocks, connections, dt=0.01, log=True)\n",
-    "    \n",
-    "# Run the simulation for some time\n",
-    "Sim.run(4*tau)"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "id": "12",
-   "metadata": {
-    "raw_mimetype": "text/restructuredtext"
-   },
-   "source": [
-    "Due to the object oriented and decentralized nature of PathSim, the :class:`.Scope` block holds the recorded time series data from the simulation internally. It can be accessed by its ``read`` method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read the data from the scope\n",
-    "time, [data_step, data_response] = Sco.read()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14",
-   "metadata": {},
-   "source": [
-    "or plotted directly in an external matplotlib window using the `plot` method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot the results from the scope\n",
-    "fig, ax = Sco.plot()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:base] *",
-   "language": "python",
-   "name": "conda-base-py"
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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