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This doctoral thesis investigates the modeling and control of fish hydrodynamics. The study consists of two main components: computational modeling of flow fields and sensory cues, and understanding the optimality principles driving fish behavior. The thesis emphasizes the incorporation of hydrodynamic interactions to accurately represent the fish’s environment. The research utilizes reinforcement learning, Bayesian inference, and high-performance computing to analyze natural behavior and flow fields. The insights gained from this study have potential applications in autonomous robot swimmers and may inspire new experiments in biology. Efficient and scalable implemen- tations of computation fluid dynamics, reinforcement learning, and Bayesian inference algorithms are developed to address the computational challenge and pave the way for future advancements in the field.