Quanscient MultiphysicsAI for PMUT design
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Designing micromachined piezoelectric ultrasonic transducers for biomedical imaging and sensing applications requires balancing competing performance goals such as sensitivity and bandwidth while meeting strict frequency targets. Traditional sequential simulation-build-test cycles provide limited visibility into the overall design space. This white paper introduces the Quanscient MultiphysicsAI workflow, which combines scalable cloud-based multiphysics simulation with precise AI surrogate modeling to enable rapid inverse design. Through a case study optimizing four geometric parameters over 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 computing resources.
