Innovative simulation and test solutions for trustworthy data-centric AI

Trustworthy AI begins with trustworthy data. Discover how Siemens and KU Leuven are performing research along this line, aiming to enable trustworthy data-centric AI for engineering applications. We consider the rich variety of engineering data, from modeling and simulation to test data acquisition, throughout product design and validation up to real-world operation. Check our blog to learn about our research towards trustworthy data-centric AI, together with KU Leuven in the frame of our research project SATISFY.AI, with support from the Flanders Innovation & Entrepreneurship Agency (VLAIO).

ML for Industrial CAE – “Just scale it”

By scaling up ML, engineers can more quickly iterate and optimize their product design, thanks to faster simulations. ML can speed up traditional methods, which makes it easier to integrate their capabilities within established company software and processes. Read about our ML engineering innovation journey in the frame of the research project ML4SIM!

GenAI Automation through code generaiton

The role of Generative AI in simulation tool automation: From zero to hero

Imagine being able to express your simulation queries in natural language, with the software dynamically responding to carry out tasks and provide solutions. This development could democratize the use of advanced tools from the Simcenter portfolio making them more accessible to users with varying levels of expertise.

From outdated to up-to-date: Modernizing deprecated code with multi-agents

Automation in Simcenter STAR-CCM+ with Java Macro Automation in Simcenter STAR-CCM+ has revolutionized how users interact with CFD simulations, providing…

Final design

Heat exchanger – Understanding my latest invention

Have you ever wondered what makes a maker and what the future of engineering might look like? Let’s join an inventor journey, exploring his heat exchanger design.