Terahertz　(THz)　in-line　digital　holography　is　a　promising　full-field,　lens-free,　and　quantitative　phase-contrast　imaging　method　with　an　extremely　compact　and　stable　optical　configuration.　Hence,　it　is　suitable　for　the　application　of　THz　waves.　However,　the　inherent　twin-image　problem　can　impair　the　quality　of　its　reconstructions.　In　this　study,　a　novel　learning-based　iterative　phase　retrieval　algorithm,　termed　as　physics-enhanced　deep　neural　network　(PhysenNet),　is　introduced.　This　method　combines　a　physical　model　with　a　convolutional　neural　network　to　mitigate　the　twin-image　issue　in　THz　waves.　Notably,　PhysenNet　can　reconstruct　the　complex　fields　of　a　sample　with　high　fidelity　from　just　a　single　in-line　digital　hologram,　without　the　need　for　constraints　or　a　pre-training　labeled　dataset.　Based　on　simulations　and　experimental　results,　it　is　evident　that　PhysenNet　surpasses　existing　phase　retrieval　algorithms　in　imaging　quality,　further　enhancing　the　application　range　of　THz　in-line　digital　holography.　©　2023　Universitat　zu　Koln.　All　rights　reserved.