diff --git a/_teaching/2025-spring-teaching-1.md b/_teaching/2025-spring-teaching-1.md
index 89f3f9b88d9c7..64a187d32caa0 100644
--- a/_teaching/2025-spring-teaching-1.md
+++ b/_teaching/2025-spring-teaching-1.md
@@ -8,7 +8,7 @@ date: 2025-04-01
 location: "Xi'an, China"
 ---
 
-This is a description of a teaching experience. You can use markdown like any other post.
+Data-Driven Solid Mechanics represents a paradigm shift in the classical field of solid mechanics by integrating modern data science and machine learning methodologies with fundamental mechanical principles. This course explores how data-driven approaches can enhance our understanding and prediction of material behavior, structural responses, and mechanical systems. Students will learn to harness experimental data and numerical simulations through advanced statistical methods, neural networks, and deep learning architectures to model complex mechanical phenomena. The curriculum spans from traditional constitutive modeling to contemporary topics such as physics-informed neural networks (PINNs), reduced-order modeling, and inverse problems in mechanics. By bridging conventional mechanics theories with cutting-edge data science techniques, this course empowers students to tackle challenging problems in material characterization, structural health monitoring, and mechanical design optimization. Special emphasis is placed on maintaining mechanical consistency while leveraging the power of data-driven methods.
 
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