Prediction of aerodynamic heating under different flight conditions is a critical and challenging step in developing a new hypersonic vehicle.The prediction model usually involves a large number of variables,and this makes genetic programming converge too slow.This paper presents a fast mathematical modelling method,divide and conquer,for aerodynamic-heating predictions.It can use the separability feature of the target model to decompose a high dimensional function into many low dimensional sub-functions.The separability is detect-ed by a special algorithm,bi-correlation test(BiCT),and the sub-functions could be determined by general ge-netic programming(GP)algorithms one by one.Thus the computational cost will be increased almost linearly with the increase of function dimension.This can help to break the curse of dimensionality,and greatly im-proved the convergence speed to get the underlying target models from a set of sample data.