Jellyfish have extensively played as inspirational test-cases for building bio-inspired and bio-hybrid robotic actuators [1]. Their propulsive features make the Medusozoan ancestor a perfect model for drafting engineered solutions for soft actuators and soft swimmers. However, the replication of the biological performance and manoeuvrability can only be achieved by understanding the neuro-mechanical excitation mechanism and their correlation with flow features. We propose a multiphysics computational model to fulfil this knowledge gap and to support the design of new-generation soft robots. Such a model consists of the sequential solution of the electrophysiological, elastic and fluid governing equations. The neuronal activity is described by the monodomain model [2], whereas the muscle contraction is enforced by a curvilinear active strain approach [3]. Both muscle activation parameters, and material properties have been tuned to match observations from in-vivo experiments. Eventually, the locomotion chain is completed by the numerical integration of the incompressible Navier-Stokes equations. The partitioned integration technique allows to tackle each of the sub-problems with the most suitable numerical method. In this context the excitation-contraction dynamics is solved via a NURBS-based Isogeometric analysis, whereas the flow field is advanced by a second-order accurate finite-difference scheme. The described computational framework allows to correlate in space and time the sensory input of the rowing jellyfish with kinematic and energetic locomotion features. REFERENCES [1] Ricotti, L. et al., “Biohybrid actuators for robotics: A review of devices actuated by living cells”, Science Robotics, 2(12), 2017. [2] Nardinocchi, Paola, and Luciano Teresi. "On the active response of soft living tissues" Journal of Elasticity 88.1: 27-39, 2007. [3] Nitti, A. et al. “A curvilinear isogeometric framework for the electromechanical activation of thin muscular tissues”, Computer Methods in Applied Mechanics and Engineering, 382, 113877, 2021.