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Designing piezoelectric micromachined ultrasonic transducers for biomedical imaging and sensing applications requires balancing competing performance objectives like sensitivity and bandwidth while meeting strict frequency targets. Traditional sequential simulation-build-test cycles offer limited visibility into the global design space. This whitepaper demonstrates the Quanscient MultiphysicsAI workflow, which unites scalable cloud-based multiphysics simulation with accurate AI surrogate modeling to enable rapid inverse design. Through a case study optimizing four geometric parameters across 10,000 coupled piezoelectric-structural-acoustic simulations, the approach achieves validated performance improvements with minimal engineering overhead, transforming days of manual iteration into seconds of transparent, data-driven exploration on standard computational resources.
