The　accurately　and　rapidly　delineating　surface　contours　is　crucial　for　ultrasonic　immersion　nondestructive　testing　(NDT)　of　complex　curved-surface　components.　However,　directly　employing　ultrasonic　signals　for　interface　reconstruction　in　dual-layer　media　remains　a　challenge.　In　this　article,　a　physics-informed　neural　network　(PINN)　is　developed　for　the　reconstruction　of　the　interface　between　water　and　unknown　surface　components.　By　incorporating　the　nonlinear　equations　of　Fermat＇s　principle　as　additional　constraints　in　the　loss　function,　a　neural　network　is　constructed　that　is　dual　driven　by　both　data　and　physical　information.　The　simulation　and　experimental　results　show　that　the　maximum　error　(ME)　of　the　interface　reconstruction　by　this　method　is　below　0.5　mm,　the　average　relative　error　(ARE)　is　less　than　1.0%,　and　the　computation　time　is　less　than　1　s.　The　imaging　results　of　V-shaped　crack　defects　using　different　interface　reconstruction　methods　are　further　compared,　verifying　the　significant　advantages　of　PINN　in　ultrasonic　array　inspection　of　complex　curved　components.　©　1963-2012　IEEE.