Method for determining a soot mass for loading a particle filter with soot particles from an exhaust gas mass flow from a combustion engine and/or a soot mass for a regeneration of the particle filter , with the aid of at least one physical model that describes the physical interactions during operation of the combustion engine based on measured relevant parameters and/or relevant characteristic data, wherein, in the at least one physical model, non-measurable variables, such as loading and filtering efficiency, are determined based on measurable or other calculated variables, such as exhaust gas mass flow, pressure and temperature upstream of the particle filter , a temperature in the particle filter , an oxygen concentration in the exhaust gas mass flow and a raw emissions mass flow, wherein the at least one physical model comprises various sub-models (f1, f2, f31, f32, f4, f5, f6) containing measured relevant parameters and/or relevant characteristic data for the respective sub-model (f1, f2, f31, f32, f4, f5, f6), characterized in that each of the sub-models (f1, f2, f31, f32, f4, f5, f6) is supplemented with a data-based error model (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ), wherein the data-based error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ) are based at least in part on the relevant parameters and/or relevant characteristic data for the respective sub-model (f1, f2, f31, f32, f4, f5, f6), wherein each of the error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ) is in the form of a Gaussian process model, such that the soot mass for loading and the soot mass for the regeneration of the particle filter are ascertained based on the sub-models (f1, f2, f31, f32, f4, f5, f6) of the physical model that are supplemented with the error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ), wherein the soot mass for loading and the soot mass for the regeneration of the particle filter are ascertained based on the sub-models (f1, f2, f31, f32, f4, f5, f6) of the physical model that are supplemented with the error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ) . wherein, in the at least one physical model, non-measurable variables, such as loading and filtering efficiency, are determined based on measurable or other calculated variables, such as exhaust gas mass flow, pressure and temperature upstream of the particle filter , a temperature in the particle filter , an oxygen concentration in the exhaust gas mass flow and a raw emissions mass flow, wherein the at least one physical model comprises various sub-models (f1, f2, f31, f32, f4, f5, f6) containing measured relevant parameters and/or relevant characteristic data for the respective sub-model (f1, f2, f31, f32, f4, f5, f6), characterized in that each of the sub-models (f1, f2, f31, f32, f4, f5, f6) is supplemented with a data-based error model (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ), wherein the data-based error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ) are based at least in part on the relevant parameters and/or relevant characteristic data for the respective sub-model (f1, f2, f31, f32, f4, f5, f6), wherein each of the error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ) is in the form of a Gaussian process model, such that the soot mass for loading and the soot mass for the regeneration of the particle filter are ascertained based on the sub-models (f1, f2, f31, f32, f4, f5, f6) of the physical model that are supplemented with the error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ), wherein the soot mass for loading and the soot mass for the regeneration of the particle filter are ascertained based on the sub-models (f1, f2, f31, f32, f4, f5, f6) of the physical model that are supplemented with the error models (ε 1 , ε 2 , ε 31 , ε 32 , ε 4 , ε 5 , ε 6 ) . <EOS>