VORtech Blog

Tools like GitHub Copilot, ChatGPT based code assistants, and similar systems have quickly found their way into the daily workflow of software engineers. These assistants represent a significant shift in how we build software. They blend machine learning with long established software engineering practices in a way that is now mature enough for real use in industry.

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.

In this blog series, we demonstrate that our mathematical strengths are an important complement to our programming skills. Using a mathematical approach, we achieve solutions that programming alone wouldn't. The blogs are based on interviews with colleagues who recently completed projects where mathematics played a decisive role. In this first post, we speak with Maarten, who conducted a fascinating project for Wageningen University & Research (WUR). He worked on efficiently storing and processing a vast amount of genetic data

We're seeing increasing attention to environmental concerns and CO2 emissions in the world of computing software. This is not without reason: according to the International Energy Agency (IEA), data centers are already responsible for approximately 1-1.5% of global electricity consumption. Writing elegant code can make a difference.

VORtech has won an important tender from TenneT in close collaboration with Tata Consultancy Services (TCS) Netherlands. For at least three years, we will be helping to develop software for calculations around the high-voltage network. 

VORtech won a tender from Rijkswaterstaat Water Verkeer en Leefomgeving (WVL) for the renewal of strategic passenger transport models.

There have been some staff changes within VORtech. We welcomed two new Scientific Software Engineers who we would like to introduce to you. The administration department had a few big changes. We had to say goodbye to two dear colleagues and we welcomed 2 new colleagues.

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.