MultiphysicsAI has proved to be a path-breaking tool for design optimization of piezoelectric micromachined ultrasonic transducers (PMUTs). This discovery is a big step forward for the field of biomedical engineering. This uniquely powerful approach combines cloud-based FEM simulation with neural surrogates. Consequently, it converts the design process from a generally heuristic and subjective trial-and-error iteration into a structured, quasi-objective form of inverse optimization. With the help of this technology, engineers can succeed in realizing validated performance improvements with minimal overhead, accelerating development of life-saving ultrasonic devices.
With the MultiphysicsAI platform, the company can quickly identify the best PMUT designs. It takes this data-driven process and completes it in seconds, rather than the days that manual iterations often require. Running on standard computational resources, it now efficiently runs upwards of 10,000 coupled piezoelectric-structural-acoustic simulations to optimize four geometric parameters. This improved efficiency speeds up the design process and more importantly, it increases the accuracy of those results.
Performance Enhancements Through AI
MultiphysicsAI combines these AI surrogates together to improve performance. With this awe-inspiring holistic approach, they have achieved an amazing 1% mean error in their predictions. This level of accuracy allows for sub-millisecond inference. It gives valuable input to their key performance indicators including transmit sensitivity, center frequency, fractional bandwidth and electrical impedance. That capacity to rapidly evaluate such indicators is an inestimable asset for engineers engaging in making complex biomedical applications.
Our AI surrogates trained extensively on 10,000 randomised geometries. Today, they provide invaluable lessons that help to inform the design trade-offs between sensitivity versus bandwidth. The Pareto front optimization process employed by MultiphysicsAI is responsible for some amazing breakthroughs. It increases the fractional bandwidth from 65% to a full 100% and increases the sensitivity by 2-3 dB. In addition, the center frequency is very constant at 12 MHz with a coefficient of variation of ±0.2%.
Streamlined Workflow for Specialists
The platform leverages MultiphysicsAI artificial intelligence to not only speed through the design lifecycle, but to put advanced capacity in the hands of industry experts. So engineers can work through complicated design trade-offs quickly. This gives them the time and space to prioritize innovation over getting lost in long cycles. As a result, the approach provides proven performance boosts in mere minutes and encourages a faster workflow that takes advantage of readily available cloud infrastructure.
This groundbreaking advancement in PMUT design represents a larger trend toward data-driven approaches in biomedical engineering. Advanced physics-based simulation techniques coupled with machine learning capabilities unlock richer insights. These tools open up new possibilities for understanding the multi-faceted relationships between design parameters and performance outcomes.