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Machine Learning

Many of our clients are working on software for forecasting or simulating natural processes such as the flow of water and the processes in industrial installations. Given the rapid developments in AI, an obvious question is whether it is also suitable for these calculations with a physics background. The answer is: yes, but…

Our anniversary symposium on April 9th ​​in Delft will discuss the big changes in our field. This blog post offers a preview.

Complex numerical simulations can be accelerated by training so-called surrogate models using machine learning. Such models give an approximation of the full model but with significantly lower computation times. It enables the use of the model in online control (model-in-the-loop) and allows interactive use and more extensive parameter studies.

Machine learning projects aren't just about developing models. Equally important are the infrastructure and supporting tools that ensure repeatable experiments, well-managed data, and reliable reproduction of results. Based on our experience, we recommend TensorFlow as a framework, MLflow for experiment tracking, and proper data management with DVC (Data Version Control).

Can we reliably forecast and manage urban flooding with a machine learning model? That was a question that was raised at Deltares, a knowledge institute for water and the subsurface. Initial research was promising. As VORtech is building up its expertise in machine learning for physics-based modelling, we welcomed the opportunity to join Deltares and Delft University of Technology in further explorations.

If you're interested in the role machine learning will play in developing computational software, Professor Rob van Nieuwpoort's group at LIACS in Leiden is a good starting point. They're working on generating efficient code for specialized hardware and developing foundation models for technical applications. In this interview, Professor Van Nieuwpoort discusses his group's research.

Although there are still plenty of doubts about the real impact of AI, there is one sector where its effect can no longer be denied: software development. VORtech quickly started working with the new AI possibilities. But not without the necessary safeguards because there are indeed risks.

The launch of ChatGPT made the world aware that Machine Learning will fundamentally change the way we live and work. The field of computing and simulation will be no exception. But it’s not just ChatGPT: there are other innovations from machine learning that are bringing exciting changes for computing. In this blog, we’ll explore the potential of this new technology.

How can machine learning be used to optimize production processes with data from machines? And how to approach this? This whitepaper offers a step-by-step plan.

The FailurePredictor is a concept to make electricity grids more robust and reliable using a combination of expert experience and machine learning.