Forces generated by the muscles actuating the fingers are transmitted through a complex network of tendons. Current models of the hand either ignore or simplify the structure of these networks [1]. It has been shown that the deformable nature of these tendinous networks results in a nonlinear transformation of muscle forces [2]. Our long-term objective is to understand how the topology of this network affects the control of finger force and motion. To achieve this, we will use a machine learning approach to evolve models of this network that can best replicate experimental results [3]. Here we present an anatomically realistic solver developed to model mechanical force transmission by a network of tendons in the human fingers. While most existing solvers neglect mechanics of tendon networks, there has been recent work on dynamic simulators accounting for tendon-bone interactions [4]. The solver we present here advances work in this field by being able to simulate mechanics of complex networks wrapped on arbitrarily shaped objects (like bones), and can be effectively used to model isometric force production in complex biomechanical systems. Its speed makes it an ideal simulation engine for the evolutionary algorithms we use to infer complex anatomical structures from sparse experimentation [3].

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