Therefore, a methodology to obtain reliable ANN-based models from small data sets would be extremely valuable because it could significantly expand the scope of applying ANN models. In the present work we investigated new approaches to apply neural ODEs for kinetic modelling of CO2 hydrogenation to hydrocarbons using small data sets. To solve the small data problem and to improve the predicting ability of the models, the architecture of ANN was constrained by integrating the kinetic and thermodynamic knowledge. The efficiency of these new ANN models was validated by carrying out numerical experiments using the LHHW-type model of CO2 hydrogenation to methane from literature . The suggested approach was finally applied to a more challenging problem: the kinetics of CO2 hydrogenation into hydrocarbons was modelled based on real measured experimental data.