diff --git a/intro-teaser.ipynb b/intro-teaser.ipynb
index 2c03e72..b46786d 100644
--- a/intro-teaser.ipynb
+++ b/intro-teaser.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "Let's start with a very reduced example that highlights some of the key capabilities of physics-based learning approaches. Let's assume our physical model is a very simple equation: a parabola along the positive x-axis.\n",
     "\n",
-    "Despite being very simple, for every point along there are two solutions, i.e. we have two modes, one above the other one below the x-axis, as shown on the left below. If we don't take care a conventional learning approach will give us an approximation like the red one shown in the middle, which is completely off. With an improved learning setup, ideally, by using a discretized numerical solver, we can at least accurately represent one of the modes of the solution (shown in green on the right).\n",
+    "Despite being very simple, there are two solutions for every point along x, i.e. we have two modes, one above the other one below the x-axis, as shown on the left below. If we don't take care a conventional learning approach will give us an approximation like the red one shown in the middle, which is completely off. With an improved learning setup, ideally, by using a discretized numerical solver, we can at least accurately represent one of the modes of the solution (shown in green on the right).\n",
     "\n",
     "```{figure} resources/intro-teaser-side-by-side.png\n",
     "---\n",