Leveraging Scientific Machine Learning to Augment Engineering Modelling and Simulation

Monday, 27 October 2025 | Online

11:00 (Bengaluru)
06:30 (Berlin) | 05:30 (London)

The world of engineering simulation is undergoing a significant paradigm shift. While traditional physics-based modelling remains the bedrock of design and analysis, its computational expense can limit the pace of innovation. This webinar explores the transformative frontier of Scientific Machine Learning, a powerful approach that fuses the rigour of physical laws 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 potentials in product development.

Join this webinar to understand how to augment, not replace, your established simulation workflows. We will delve into practical applications, from creating highly accurate surrogate models for rapid design exploration to accelerating multi-physics solvers. Through real-world examples, we will understand how Scientific Machine Learning can help you tackle previously intractable problems, optimize complex systems with greater efficiency, and gain a definitive competitive advantage by making your simulations faster, smarter, and more insightful. The session ends by highlighting the much needed aspects of Verification, Validation and Uncertainty Quantification (VVUQ) of Scientific Machine Learning models.

Who should attend?

• Engineers, Simulation analysts, R&D scientists and technical managers involved in product design, modelling, and analysis.

What you'll take away

• A clear understanding of what Scientific Machine Learning is and how it enhances traditional simulation.
• Insight into practical methods for building Scientific Machine Learning-based surrogate models like Neural Operators or Physics-Informed Neural Networks.
• The ability to identify key opportunities to apply Scientific Machine Learning within your own engineering workflows.
• A clear direction to implement a VVUQ framework that can ensure safe and robust application of SciML.

A​genda

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.