Simulation is of large importance to discovering and developing advanced materials. However, simulation is often computationally expensive and time-consuming, especially when exploring large configuration spaces or optimizing material properties across multiple scales. Further complicating the matter, simulations are subject to a variety of uncertainties including to initial conditions, boundary conditions, and choice of model form and parameter values.
AI and Machine Learning (ML) tools including design of experiments, predictive ML models, and statistical calibration can help by significantly reducing the computational cost of simulation, achieving more accurate simulation, and providing a systematic approach to managing uncertainty. Join us for this webinar in which SmartUQ principal application engineer, Gavin Jones, will introduce the use of SmartUQ AI and Machine Learning software for materials simulation applications.
