I'm having difficulty with multidimensional Fourier Transforms. I have the following problem for $u=u(t,x) \in \mathbb{R}$
$$ \frac{\partial u}{\partial t} = \sum_{m,n=1}^d a_{mn}\frac{\partial^{2}u}{\partial x_m\partial x_n} + \sum_{n=1}^d b_n\frac{\partial u}{\partial x_n} + cu $$
$$ u(0,x) = \phi(x) $$
Where $A=a_{mn}$ is a positive definite, $d\times d$ matrix, $b=b_n \in \mathbb{R}^d$ is a vector of constants, $c$ is a constant and $\phi \in \mathscr{S}^d$ (Schwartz/rapidly decreasing). We are asked to solve this using the Fourier Transform.
There were two approaches I took, but I got stuck on both. The first was to transform $(x, t) \to (k, t)$ space which gives
$$ \frac{\partial \hat{u}}{\partial t} = -(k^TAk)\hat{u} + i\langle b,k\rangle \hat{u} + c\hat{u} $$
Solving for $\hat{u}$
$$ \hat{u}(t,k) = \hat{\phi}(k)\exp([-(k^TAk) + i\langle b,k\rangle + c]t) $$
Now, because A is diagonalisable, we have that
$$ -(k^TAk) = -(k^TQDQ^Tk) $$
Setting $\eta^T = k^TQ$ we find
$$ \hat{u}(t,k) = \hat{\phi}(k)\exp(ct)\exp([-(\eta^TD\eta) + i\langle b,k\rangle]t) $$
Now, $\eta$ is a vector of constants and $D$ is a diagonal matrix of eigenvalues, so we get
$$\exp(-(\eta^TD\eta)t) = \exp (-(\lambda_1\eta_1^2 + \lambda_2\eta_2^2 + .. + \lambda_n\eta_n^2)t) $$
And this is where I get stuck.. I have absolutely no idea how to get this back into $u(t,x)$ in terms of a convolution inside the integral. I don't even know how to write the product of eigenvalues and constants more succinctly, as I don't think it is correct to write $\langle \lambda,\eta^2 \rangle$ nor is it correct to write $\langle \lambda, \lvert \eta\rvert^2 \rangle$. Is the final result just a product of multiple integrals with convolution ie
$$ u(t,x) = \frac{1}{(2\pi)^{\frac{d}{2}}}\int_\mathbb{R^d} \exp(ikx)\exp(-\lambda_1\eta_1^2) dk \int_\mathbb{R^d} \exp(ikx)\exp(-\lambda_2\eta_2^2) dk... $$
with the convolution term somewhere in the integral?
Note: I also tried to ansatz a solution, setting $u(t,x) = v(t,x+bt)$ to remove the
$$ \sum_{n=1}^d b_n\frac{\partial u}{\partial x_n} $$
term but it didn't work.