Understanding the neuromechanical principles of collective motor behaviors is a longstanding question at the intersection between neuroscience, motor control and fluid mechanics. Striking examples of these behaviors include the patterns formed by fishes swimming or birds flying in schooling formations. What is the advantage of schooling? What central vs peripheral control mechanisms are involved in these emergent behaviors? Modeling the closed loop interaction between the neural and mechanical components describing collective behaviors is a complex task. It requires accurate modeling and simulating two parts: the dynamics of neural circuits and muscles (controller) and the dynamics of the bodies (i.e. animals/robots), the environment and their interaction (physics). In the case of fluids, the physics is even harder to simulate, as one has to account for the interactions between bodies and the fluid. Previous fluid solvers have been developed, but typically considered overly simplified bodies, controllers or simplified fluid simulations [1,2,3,4,5]. Although useful to explore the locomotion of single animals, these models cannot capture the neuromechanical components of collective behaviors. For this reason we designed a new physics fluid-structure interaction solver that integrated with multi-body rigid simulations, biological neural networks and virtual muscle models allows for the closed simulations of multi-animal locomotion in fluid. We apply the solver to study swimming in single adult zebrafish. Our initial investigation on individual fishes suggests that stretch feedback could lead to an increase in frequency and speed. We now plan to test the role of stretch feedback in zebrafish schooling. This could allow us to disentangle the neuromechanical principles and the advantages of schooling behaviors. [1] Gazzola, M., Chatelain, P., Van Rees, W. M., \& Koumoutsakos, P. (2011). Simulations of single and multiple swimmers with non-divergence free deforming geometries. Journal of Computational Physics, 230(19), 7093-7114. [2] Bergmann, M., \& Iollo, A. (2011). Modeling and simulation of fish-like swimming. Journal of Computational Physics, 230(2), 329-348. [3] Porez, M., Boyer, F., \& Ijspeert, A. J. (2014). Improved Lighthill fish swimming model for bio-inspired robots: Modeling, computational aspects and experimental comparisons. The International Journal of Robotics Research, 33(10), 1322-1341. [4] Ferrario, A., Palyanov, A., Koutsikou, S., Li, W., Soffe, S., Roberts, A., \& Borisyuk, R. (2021). From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals. PLoS Computational Biology, 17(12), e1009654. [5] Todorov, E., Erez, T., \& Tassa, Y. (2012, October). Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ international conference on intelligent robots and systems (pp. 5026-5033). IEEE.