# $H^1$ Inner Product for vector valuled functions

I am not sure how to find the $$H^{1}(D)$$ inner product for two functions $$u,v: D \rightarrow \mathbb{R}^2$$, ($$D \subset \mathbb{R}^2$$). The inner product for scalar functions is defined as:

$$\int_{D} f \; g \; dx + \int_{D} \nabla f \cdot \nabla g \; dx$$

For extending this definition to vector valued functions, I found this link (Inner product for vector - valued functions) but it treats only the first term ($$L^2$$ norm). For the second term ($$H^1$$ seminorm), I tried to look up definitions of inner product for matrices but found multiple answers. Can someone please tell me which is the correct way to compute this?

Edit: I need to compute it for calculating the Gramian Matrix for a finite set of vector valued functions, with respect to the $$H^1(D)$$ norm. Is this the right way to do it?

Thank you!

$$H^1(D;\mathbb{R}^2)=\{u \in L^2(D;\mathbb{R}^2) : \nabla u \in L^2(D;\mathbb{R}^{2 \times 2}) \}$$
Let $$u,v :D \to \mathbb{R}^2$$. Let us denote by $$u\cdot v=\sum_{i} u_i v_i$$ the vector scalar product and by $$A:B=\sum_{i,j} a_{ij} b_{ij}$$ the matrix scalar product. The inner product of $$H^1(D;\mathbb{R}^2)$$ is given by
$$(u,v)_{H^1}=\int_D u \cdot v + \nabla u : \nabla v \text{ d}x.$$