NAFEMS Americas and Digital Engineering (DE) teamed up (once again) to present CAASE, the (now Virtual) Conference on Advancing Analysis & Simulation in Engineering, on June 16-18, 2020!

CAASE20 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, unlike any other, to share experiences, discuss relevant trends, discover common themes, and explore future issues, including:
-What is the future for engineering analysis and simulation?
-Where will it lead us in the next decade?
-How can designers and engineers realize its full potential?
What are the business, technological, and human enablers that will take past successful developments to new levels in the next ten years?

Resource Abstract

The Digital Revolution has fundamentally transformed the role that predicting product performance plays in the business model of companies. Customers are increasingly buying directly the outcomes and experiences that result from the product use rather than the products themselves.

This fundamental shift in customer attitudes now requires for companies to not only design products, but to also accurately predict the outcomes these products produce based on actual customer usage and moreover tune, in real-time, the performance of these products to desired customer expectations. Engineering Simulation as it stands today is simply not up to this task.

Physics Informed Machine Learning (PIML™) technology converts complex and time-consuming engineering simulation workflows into fast running instantaneous solvers. PIML™ in contrast to purely Data-Driven Machine Learning techniques, is much more accurate, requires significantly less data, gives users insight as to how to improve the solver’s predictive capability, is more robust to changes in the environment and can be integrated into Bayesian information fusion frameworks.

Applications include:

1. Simulation Democratization: PIML™ powered surrogate solvers can be integrated into your existing simulation powered design democratization initiatives.

2. Sales Accelerators: PIML™ can also be used to accelerate product sales using your company’s existing sales and distribution lines. PIML™ enables companies to create complex configurators and demonstrators that can be web-enabled or enhanced through virtual reality (VR) showcasing to customers how your products will perform under real-world complex scenarios.

3. Warranty Claim Occurrence Predictor: PIML™ could also help reduce your company’s warranty claims. We recently demonstrated to a customer that we could feed field data to our PIML™ solver and accurately predict where a warranty failure would occur.

4. Data Classification & Virtual Sensor: PIML™ can be used as classification tool to distinguish “good” data vs. “bad” data say in a test environment.

6. Model Based Controls: PIML™ speeds models to make them usable in model-based control architectures that require models to run faster than real time in application such as system performance simulation, autonomous systems, climate control, to name a few. These model-based control architectures can then eventually be incorporated into Digital Twins

7. Digital Twins: PIML™ powered surrogate solvers can be embedded into onboard processors creating Predictive Digital Twins that can provide in real-time the response of key performance parameters to input operating conditions.