Research Scientist at the Institute for Energy Technology (IFE) in Norway, I work at the intersection of computational physics, AI, numerical modeling, and scientific software development. With a PhD in plasma physics and more than 10 years of research experience, I develop and apply physics-based simulation tools across multi-physics problems, combining mathematical modeling with large-scale data processing and clear data visualization.
A key part of my work is building AI-enabled modeling pipelines, including physics-informed machine learning (PIML), Physics-Informed Neural Networks (PINNs), and surrogate modeling for faster exploration, optimization, and decision support. I also work with process modeling using finite element methods and CFD, bridging detailed simulations with reduced-order and data-driven models that can be deployed in industrial settings.
In recent years, I have expanded my focus toward digital twins, developing the data infrastructure and automation workflows needed to connect models with live and historical process data. This includes hands-on experience with Node-RED, InfluxDB, and Grafana, alongside tooling for reliable data acquisition, monitoring, and model integration.
My technical foundation spans Python, C/C++, MATLAB, Java, and Fortran, and I routinely work with finite element methods, multi-physics modeling, CFD, and Particle-in-Cell (PIC) simulations. I’m motivated by advancing energy technology and accelerating sustainable industrial innovation, and I enjoy collaborating with partners and peers on research, applied engineering, and AI-driven digital twin solutions.
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