GPR	Gaussian Process Regression
RT	Regression tree
UQ	Uncertainty quantification
DED	Directed energy deposition
DED-LB/M	Laser-based directed energy deposition using metal powder
LPBF	Laser powder bed fusion
FFF	Fused filament fabrication
EBM	Electron beam melting
CAD	Computer aided design
ARD	Automatic relevance determination
MAE	Mean absolute error
v	Velocity
P	Laser power
$\dot{m}$	Powder flow rate
$d_{\mathrm{L}}$	Laser beam diameter on the surface of the substrate
$d_{\exp }^2$	Expected squared deviation between prediction and target value
z	Target value of geometry characteristic
$y\left(\mathbf{x}\right)$	Prediction performed with the GPR model
$d_{\mathrm{w}}$	Depth of of a single DED track
w	Depth of a single DED track
h	Height of a single DED track
$R^2$	Coefficient of determination
$\mathbf{q}\left(\mathbf{x}\right)$	Vector with linear basis functions
$\mathit{\beta }$	Coefficients of linear basis
k	Kernel of GPR model
$l_m$	Length scale
${\sigma }_f^2$	Signal variance
${\delta }_{ij}$	Kronecker delta
$y_{\mathrm{test}}$	Measured values in the test data set
${\overline{y}}_{\mathrm{test}}$	Mean value of $y_{\mathrm{test}}$