AI, Data Driven Models & Machine Learning: How Will Advanced Technologies Shape Future Simulation Processes?

Date: 23-24 March 2021 (Tentative)
Location: Virtual Seminar

Overview

Artificial Intelligence (AI), aside from being a media-friendly hot-topic, is becoming more and more a part of the activities traditionally covered by the engineering analysis and simulation community. Recent advances in the application of AI, machine learning (deep learning) and predictive analytics, have brought these technologies to the fore in every area of industry. This virtual event will bring together speakers from the end-user, consultancy, and academic industries to discuss where we are and how these technologies are being used to advance significantly the engineering analysis and simulation capabilities and approaches over the next 10 years.

This event will address such questions as…

• How do we benchmark the needs for an AI data driven process?
• How will engineering data science drive the future for engineering simulation?
• How can the knowledge built on engineering simulation help guide the use of these advanced technologies?
• Conversely, how can advanced technologies drive the future of engineering simulation?
• VVUQ and trustworthiness in results is important, so how does that apply to AI?
• In what areas does AI compete, complement, and/or integrate with engineering simulation methods and processes?

Description

NAFEMS Americas and invited speakers will address these questions, and more, at, “AI, Data Driven Models & Machine Learning: How Will Advanced Technologies Shape Future Simulation Processes?" This virtual event aims to deliver information and insights on critical topic areas in a manner that maximizes the take-away value for attendees. An event agenda and concept championed by several leading figures in the industry will provide the opportunity to learn about the latest technologies and practices, which attendees can later share and apply within their own organizations.

More information to be provided shortly.