Medical image registration plays a key role in radiation-based cancer treatment in the thorax. Naturally thorax image registration is highly patientspecific, requiring registration models to be trained patient individually. The available per-patient data is highly limited and often does not cover all potential scenarios occurring during treatment. In thisworkwe create patient-individual implicit neural representations (INRs) that represent the displacement fields during a breathing cycle. We tackle the data shortage by including physical knowledge, while simultaneously improving generalization capabilities. Our results show that physical constraints can be well integrated into INRs. However, we find that extrapolation capabilities are highly dependent on the induced physical regularization.