From f575c6471468ac0e19d781c16e7d4612e714f8a7 Mon Sep 17 00:00:00 2001
From: yjwang <72957567+wangyji@users.noreply.github.com>
Date: Thu, 8 Aug 2024 14:57:10 -0400
Subject: [PATCH] Update README.md

---
 tutorial/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorial/README.md b/tutorial/README.md
index b265a97..7a91e80 100644
--- a/tutorial/README.md
+++ b/tutorial/README.md
@@ -6,7 +6,7 @@ This folder contains a tutorial example for inferring the effective viscosity $\
 shelves using synthetic data of ice velocity and thickness via physics-informed neural networks 
 (PINNs). Both the simulation code for synthetic data generation and the PINN code for viscosity
 inversion are included. All codes are well-documented for easy understanding. Additionally, we
-have provided a [Colab notebook](https://colab.research.google.com/github/YaoGroup/DIFFICE_jax/blob/main/tutorial/colab/train_syndata.ipynb) that allows users to run the code online in Google Colab.
+have provided a [Colab Notebook](https://colab.research.google.com/github/YaoGroup/DIFFICE_jax/blob/main/tutorial/colab/train_syndata.ipynb) that allows users to run the code online in Google Colab.
 
 ## Forward problem setup
 Considering the floating ice moving in a given domain, the synthetic data of ice velocity