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

Date: 28-29 April 2021
Location: Online International Seminar

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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 addressed 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?

Event Agenda

The event agenda, located at the top of this page, is subject to change.

All times are Eastern Daylight Time.

We recommend that you use Google Chrome to join this event as it works best with the conference platform.

Title	Speaker	Company	Start Time
Day 1: Wednesday, 28 April
Welcome & Introduction (Stage 1)		NAFEMS Americas	9:45
AI for Simulation: Current Possibilities and Future Challenges	Peter Chow	Fujitsu Laboratories of Europe Limited	10:00
Challenges with Trustworthiness of Data-Driven and Machine Learning Approaches	Mahmood Tabaddor	UL LLC	10:30
Case Studies (Parallel Presentations; Stages 1-2)
AI/ML/ROM-based Modelling, Prediction and Optimization for CAE Applications (Stage 1)	Kambiz Kayvantash	CADLM	11:00
Machine Learning for Automation of Post-processing of Simulation Results and Extraction of 3D Digital CAE Reports (Stage 2)	Mohan Varma	Visual Collaboration Technologies Inc	11:00
Main Program; Stage 1
Efficient Multiscale Composite Modeling via an Embedded Long Short Term Memory Surrogate Microscale Model	Joshua Stuckner	NASA Glenn Research Center	11:30
Case Studies (Parallel Presentations; Stages 1-2)
Combining Machine Learning and Physics-based Simulation for Product Development (Stage 1)	Yangzhan Yang	Dassault Systemes SIMULIA	12:00
Best Practices for Data Driven Simulation Modeling (Stage 2)	Danilo Di Stefano	Esteco SPA	12:00
Breakout Rooms (Parallel Discussions - Not Recorded; Rooms 1-4)
Differential Equations and Data Driven Models : Which one came first and which one is more reliable for engineering practices? (Room 1)	Kambiz Kayvantash	CADLM	12:30
Learn to Predict, or Learn to Simulate? Big Data, or Small Data? -- Reality in the Simulation World (Room 2)	Zhenyuan Gao	Dassault Systemes SIMULIA	12:30
How to Trust ML/AI Algorithms? (Room 3)	Mahmood Tabaddor	UL LLC	12:30
The Best AI is Invisible. Would you agree? (Room 4)	Fatma Kocer	Altair Engineering	12:30
Open Discussions (Additional Rooms Available)	N/A	N/A	12:30
_________________________
Day 2: Thursday, 29 April
Brief Welcome (Stage 1)		NAFEMS Americas	9:55
Learning Simulation Using Graph Networks	Tobias Pfaff	DeepMind	10:00
Data Science Driving the Future of Engineering Simulation	Robin Tuluie	Physics X	10:30
Case Studies (Parallel Presentations; Stages 1-2)
The New "AI" Wave: Does It Apply to Engineering? (Stage 1)	Pierre Baqué	Neural Concept Ltd.	11:00
AI-Powered Product Design (Stage 2)	Fatma Kocer	Altair Engineering	11:00
Breakout Rooms (Parallel Discussions - Not Recorded; Rooms 1-5)
How will real-time simulations impact the products around us? (Room 1)	Anthony Massobrio	Neural Concept Ltd.	11:30
What steps should I take to determine how my team might best utilize AI data driven models and the digital twin concept? (Room 2)	Rod Dreisbach	IEAC	11:30
How is AI impacting the way we should train tomorrow’s engineering simulation workforce? (Room 3)	Olivia Pinon Fischer	Aerospace Systems Design Laboratory	11:30
Is physics based knowledge the future of ML/AI? What is “Small Data” and are we at the cusp of a revolution in ML/AI? (Room 4)	Juan Betts	Front End Analytics	11:30
Autoencoders: Are they the future of ROM and generative design? (Room 5)	Marc Emmanuelli	Monolith AI	11:30
Open Discussions (Additional Rooms Available)	N/A	N/A	11:30
Main Program (Stage 1)
Panel Discussion: State of Explainable AI/ML	Mahmood Tabaddor	UL LLC	12:30

Summaries of the Breakout Discussion Sessions

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