What, exactly, is Scientific Machine Learning, and how can it enhance traditional simulation? What role does it play in building surrogate models like Neural Operators or Physics-Informed Neural Networks?

This webinar explores the transformative potential of Scientific Machine Learning, a powerful approach that fuses the rigour of physics-based modelling with the adaptive power of data-driven algorithms. Discover how this synergy is bridging the gap between complex simulations and real-time decision-making, unlocking new potential in product development.

The webinar will focus on enabling you to

• augment, not replace, your established simulation workflows.
• create highly accurate surrogate models for rapid design exploration
• Use Scientific Machine Learning to tackle previously intractable problems and optimize complex systems with greater efficiency
• gain a competitive advantage by making your simulations faster, smarter, and more insightful.
• understand the relevant aspects of Verification, Validation and Uncertainty Quantification (VVUQ) of Scientific Machine Learning models.

This is an opportunity to gain practical knowledge that enables you to identify key opportunities to apply Scientific Machine Learning within your own engineering workflows.

S​peaker

Anand NAGARAJAN, Airbus Technology Champion in AI & ML

Anand Nagarajan is currently a Technology Champion for Artificial Intelligence and Machine Learning at Airbus India Engineering and is part of Technical Referent Community at Airbus India. He has a Bachelor's degree in Mechanical Engineering and a gold medal in Masters degree in Robotics. He has a postgraduate specialization in Artificial Intelligence and Machine Learning from University of Texas at Austin. He also has a specialization in 'Advanced Strategic Management' from IIM Kozhikode. He has a total experience of 17 years in Industry, Academia and Research. Few of his research interests include Scientific Machine Learning, Uncertainty Quantification, Digital Twins, Additive Manufacturing and their applications in Aerospace Engineering.