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Having spent years working with engineering design, optimization, control using simulation and AI technologies, I have long envisioned a workflow where these powerful tools could collaborate autonomously under Engineer supervision. Today, Agentic AI is making this possible.

Had a nice week in the Japan office with Siemens and Altair colleagues, presenting at JSAE and a packed schedule of customer and partner visits.

When working with industrial AI, training a high performance prediction model is only one part of the challenges. Other critical aspects include:

A study of human-like autonomous driving controllers in a driving simulator just got published in Transportation Research Part F: Traffic Psychology and Behavior. The article is written by Flavia Sofia Acerbo, the industrial PhD at Siemens and KU Leuven

AI Agents and Agentic AI are increasingly considered in engineering workflows, not just for coding, reasoning, but also to learn from historical data and enhance automation. The big question remains: what real added value do they bring to engineering practice, i.e how AI Agents helps to accelerate simulation modeling, control design, or MBSE processes.

As the robotics revolution accelerates, one of our key visions is to combine simulation with real world testing across all: from control systems to AI model development, to verification and validation (V&V)

Last week, I had the pleasure visiting the Oxford Control Group to give a seminar and exchange ideas with faculty members and students. It was also great to meet the PhD student Rory Halsall, whom we jointly supervise within the collaboration between UK Research and Innovation and Siemens :).

In recent months, I’ve had the great honor of serving on PhD Defense Juries of four PhDs:

It is great to welcome partners and colleagues from the EU RobustifAI project (Robustifying Generative AI through Human-centric integration of Neural and Symbolic methods) to Siemens office for technical updates, discussions, and AI knowledge exchange.

The next wave of Industrial AI is accelerating, with physics-aware, trustworthy, and integration into engineering workflow.

The nice image comes straight from the actual research & development results. From right to left, you can see the Gaussian Splatting training process progressively improve the 3D reconstruction.

It is a pleasure working with Prof. Alisa Rupenyan-Vasileva and the Postdoc Gabriele Fadini (ZHAW Zurich University of Applied Sciences), who are actively investigating state of the art autocalibration of high-dimensional control parameters using a combination of Digital Twin technology and AI methods.