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Surgery simulators based on model-order reduction
(Elsevier Science Ltd., 2023) Quesada Granja, Carlos; Badías, Alberto; González, David; Alfaro, Icíar; Chinesta, Francisco; Cueto, Elías
The development of realistic, credible surgery simulators for surgery planning and training is not an easy task. This is due, on the one hand, to the extremely complex physics involved: large deformations of complex living tissues, cutting and tearing and contact, among others. On the other hand, a simulators requires a very fast feedback rate: some 60 Hz if we expect visual feedback, but some 500–1000 Hz if we plan to add the simulator with haptic feedback. These very stringent requirements make it difficult to combine both ingredients, accuracy and realism, with fast responses. This is why model-order reduction has opened a new avenue for the development of such simulators. Reduced-order models allow for extremely fast feedback rates without compromising accuracy; error can be controlled by means of error estimators and so can the feedback rate, by choosing judiciously the number of degrees of freedom of the model. Among all the available reduced-order modeling techniques, we focus here on the proper generalized decomposition, a technique that has shown great promise in the field.