Data-Driven Engineering:
AI/ML Trends and Insights for Engineering

14 and 15 February 2024 | Online

A​ccess Presentations and Recordings

O​bjectives

Some of the objectives for attending this event include:

1. Stay Informed About Industry Trends - Participants aim to stay updated on the latest trends and technologies in engineering data science. This includes learning about recent advancements in data science, machine learning, algorithms, techniques, and their real-world applications.
2. Knowledge Sharing and Best Practices - Attendees seek to share their expertise and exchange best practices with fellow professionals in the field. This knowledge-sharing enhances their understanding of evolving industry standards and equips them with new skills.
3. Networking Opportunities - Networking is a primary goal, as attendees aim to connect with peers, including data scientists, engineers, and machine learning practitioners. This networking can lead to job prospects, collaborative projects, and valuable learning experiences through interaction with others.
4. Application Insights - Participants want to gain insights into how data science and machine learning are effectively applied to solve real-world problems. This knowledge enables them to apply these techniques to their own work and address practical challenges.
5. Inspiration and Motivation - Attending the event allows individuals to draw inspiration from the work of other data scientists and machine learning professionals. This inspiration serves as a catalyst for them to pursue their own goals and aspire to achieve excellence in the field.

Agenda

Day 1 – Wednesday 14^th February

A​ll times below are in GMT (London)

15:00 Opening Day 1
Vladimir Balabanov, Boeing

15:05 Keynote Address: Smart Data, Not Big Data: Neural Inspired Sparse Sensing and Control
Bing Brunton, University of Washington

1​5:45 1-D System-Level Simulation leveraged for Engineering Controls’ Strategy Optimization
R​emi Duquette, Maya HTT Ltd.

16:10 Short break

16:20 Discussion Sessions

1. Discussion of the Keynote Address: Smart Data, Not Big Data: Neural Inspired Sparse Sensing and Control
   Led by University of Washington representative
   
2. G​enerative AI
   Led by Astrid Walle, Siemens Energy / Remi Duquette, Maya HTT Ltd.

16:45 Machine Learning with Small Data Sets
Kalyan Sharma, Ansys

17:10 Virtual Subject Matter Experts
Shane Mooney, Kinetic Vision

1​7:35 Machine Learning to Empower Engineering Organizations: Technology & Applications
Martino Milani, Neural Concept

18:00 Closing Day 1
Fatma Kocer, Altair Engineering

Day 2 – Thursday, 15^th February

15:00 Opening Day 2
Vladimir Balabanov, Boeing

1​5:05 Keynote Address: Principles and Metrics for Curating Large Structural Simulation Data sets for Machine Learning
Jami Shah, Ohio State University

15:45 Quantum Reinforcement Learning to Solve Vehicle Routing Problem
Takanori Ide, Aisin Co Ltd.

16:10 Short break

16:20 Discussion Sessions

1. Discussion of the Keynote Address: Principles and Metrics for Curating Large Structural Simulation Data sets for Machine Learning
   Led by Alex Adrian, GE Aerospace / Fatma Kocer, Altair Engineering
   
2. Data, Databases, Data Access, Interface to SDM: How to decide what to store and how?
   Simulation environment assisted by reinforcement learning
   Led by Remi Duquette, Maya HTT Ltd. / Astrid Walle, Siemens Energy

1​6:45 How we at Siemens Energy Enable Engineers to do Data Science
Astrid Walle, Siemens Energy

17:10 Convergence of CAE, Data Science and HPC
Fatma Kocer, Altair Engineering

1​7:35 Bringing Shared Context to Simulation Data to Enable AI+HPC-Driven Engineering
Sandeep Urankar, Rescale

18:10 Closing Day 2
Fatma Kocer, Altair Engineering