This whitepaper discusses how RF MEMS acoustic resonator-based filters can be efficiently and effectively designed, thereby reducing cost, risk, and time to market.
This white paper discusses how ultrasonic non-destructive testing (NDT) can be efficiently and effectively optimized, thus reducing costs and risks, with the use of accurate engineering simulations.
As a design engineer, have you ever had a senior PHD level engineering analyst come in for an 11th-hour design change? What if you could have collaborated together every step of the way through from conception to form factor to simulation to a final product?
Could OnScale's Multiphysics simulation have been the key to solving the global supply disturbance amounting to $400 million per hour? Read the article in Digital Engineering.
This webinar explores the steps to take with any CAD model to ensure that it’s simulation ready.
This webinar describes OnScale’s revolutionary approach that combines state-of-the-art proprietary multiphysics solvers and cutting-edge high-performance computers on the cloud. We also describe OnScale’s unique technology and business approach, which gives users the ability to run the thousands of simulations required to fully optimize the electronics package using a flip-chip, all in a fraction of the usual time and cost.
This white paper discusses how RF MEMS acoustic resonator-based filters can be efficiently and effectively designed, thereby reducing cost, risk, and time to market. Mathematical modeling and numerical simulation play a key role in achieving quick and reliable design wins.
Project BreathEasy is a consortium of multiphysics FEA/CFD vendors, medical device manufacturers, engineers, and doctors from around the world who are developing digital twins of the lungs of COVID-19 patients to help doctors improve patient outcomes and optimize use of limited ventilator resources in major outbreak areas.
This white paper discusses how synthetic datasets for training AI can be generated in hours using the OnScale cloud simulation platform. The demonstrated approach of using synthetic datasets to train AI networks can drastically reduce cost, risk, and time for the development of new hardware technologies.
In this Rev-Sim Guest Post, OnScale CTO, Dr. David Freed shares his views on the coming decade. Freed believes that engineering simulation will be hugely impacted by, and become inextricably entwined with, machine learning / artificial intelligence (ML/AI). Do you agree? Read all about it in this Rev-Sim Guest Post!