We offer expertise in developing and applying advanced computational methods for complex modelling and simulation problems. We specialize in creating new algorithms and transforming them into efficient, reliable, and robust software of professional quality.
We are applied mathematicians and computer scientists who, over many years, have built deep expertise in our application areas. We combine this with a strong drive to develop methods and software that can be adapted to various applications without compromising accuracy or performance.
We draw on a strong tradition at SINTEF Digital, where the development of numerical methods, algorithms, and high-quality software has been central since the early 1990s. Our goal is to create solutions that meet today's needs while remaining capable of handling tomorrow's challenges, and our broad methodological expertise lets us contribute across a wide range of fields and technology areas.
Since the autumn of 2024 we have also worked systematically on what agentic AI means for computational science. We use agents extensively in our own development work, but the more interesting question is the one running the other way: what has to change in a simulator for an agent to work with it well? Typed interfaces, informative error messages and documentation written to be read by machines as well as people turn out to matter far more than we expected. We are equally interested in agents as research assistants rather than merely as coding tools, and we deliberately gather hands-on experience to stay at the front of a technology that is moving faster than anything we have worked with before.
This work led to SINTEF AgentLab, which brings together agent-related activity across the whole of SINTEF Digital.
We combine high academic standards with a deep understanding and strong focus of industrial needs, ensuring that our solutions are both innovative and practical. What sets us apart is our ability to swiftly advance new ideas from academic research, overcoming simplifying assumptions, use our open-source software to rapidly prototype and demonstrate effectiveness in relevant environments, and bringing new research into operational use.
Our group is internationally oriented and consists of 19 permanent scientists, most with a PhD in mathematics or physics, and several associated master and PhD students. We have established strategic cooperations with leading academic institutions worldwide and long-lasting partnerships with international industry clients.
With over 20 years' experience, we combine high academic quality in research with a strong focus on industrial relevance.
To meet the future needs of our users, we have in recent years initiated research on differentiable simulators, optimization under uncertainty, integrated physics-and-data-driven methods, and practical use of quantum computing.
We are also exploring what the future of human interaction with computational models might look like by combining applied mathematics, simulation technology, and artificial intelligence. Our vision is to develop natural, conversational interfaces with simulators that allow users to set up models, seamlessly combine different simulators, analyze results, and improve simulations supported by technologies such as large language models (LLMs), retrieval-augmented generation (RAG), and AI agents, in combination with differentiable simulators.
Building on our long tradition of developing flexible and generic methods, we are now working to make simulation tools more intuitive, efficient, and intelligent—across disciplines.
We are always open to new collaboration opportunities. Contact us to learn more about our research and how we can work together to solve complex scientific and technological challenges. You can also engage with our expertise through tailored courses, strategic consulting, or by spending time with us as a visiting researcher or collaborator.
Simulation of flow in porous media involves modeling the movement of fluids through materials like soil and rock, providing insights into phenomena such as groundwater flow, oil reservoir behavior, and subsurface carbon storage. This field plays a...
Understanding geomechanical responses to subsurface operations is essential for predicting and managing risks. We develop efficient solution methods and robust discretizations on complex grids to accurately couple mechanical deformation with...
Carbon capture and storage (CCS) is a key technology for reducing greenhouse gas emissions by capturing CO₂ from industrial processes and safely storing it underground. Computational modeling is essential for understanding the physical processes...
Geothermal energy harnesses the Earth's internal heat for a reliable and sustainable power supply, while subsurface storage of thermal energy contributes to a balanced energy infrastructure.
Underground storage of natural gas—and increasingly, hydrogen—plays a vital role in ensuring energy security and system flexibility. By providing reserve capacity during periods of low demand and enabling rapid supply during peak consumption, gas...
The use of computer simulations to analyze and predict the behavior of electrochemical systems is finding widespread applications in battery technology, fuel cells, and electrolysis. As a critical tool, it enables researchers to design and optimize...
The aftermath of severe weather events, whether on a national or international scale, leads to substantial infrastructure damage, often resulting in considerable insurance claims. The anticipated rise in extreme weather incidents emphasizes the...
Accurately simulating and predicting drift in the ocean, along with its associated uncertainty, is crucial for various reasons. It is essential for maritime safety, helping to forecast the movement of objects like drifting vessels, debris, or...
Advanced mathematics and computational methods are essential across all of SINTEF’s business areas. What distinguishes us from domain experts is our emphasis on enabling technology and cross-cutting expertise, which often allows us to operate...
We develop technology that turns AI agents into genuine collaborators in demanding technical work, starting from simulation and computational science.
To research innovative computational methods and mature them to a stage suitable for commercial implementation or deployment it is imperative to develop and maintain efficient and flexible ecosystems that facilitate effective experimental programming...
Scientific computing on modern hardware explores how specialized processors, such as GPUs, can drastically accelerate scientific and engineering simulations. With nearly two decades of use in research, GPUs have proven particularly powerful thanks to...
Gridding and discretization form the backbone of numerical simulation, transforming continuous mathematical models into finite sets of discrete quantities that can be solved for on a computer. Research on new methods is essential to develop more...
Numerical solvers and algorithms make it possible to tackle large, coupled systems of equations arising from physical models. Research in this area is essential for developing simulations that are robust, scalable, and efficient – even for complex...
Hybrid data-driven and physics-informed models combine the strengths of machine learning with traditional physical simulations to enhance accuracy, speed, and interpretability.
Visual computing is a field of computing that deals with the processing of visual information in forms such as images, videos, and 3D data. Our recent focus has mainly been on visual computing for medical ultrasound, but we have also worked with...
Quantum computing is an impending technology that offers a huge potential for societal and business disruption. However, quantum computers work in a fundamentally different way than classical computers, and utilizing them requires a deep...
BattMo is an open source simulation code for continuum modelling of electrochemical devices written in Matlab and Julia
A GPU-accelerated simulation framework for running large ensembles of simplified ocean models for real-world domains.
Fimbul is a modern open-source software package developed in Julia for the simulation and analysis of geothermal reservoirs and thermal energy storage systems. The tool makes it easy to explore, compare, and optimize a wide range of geo-energy...
Experimental Julia framework for fully differentiable multiphysics simulators based on implicit finite-volume methods with automatic differentiation.
A free open-source community code for rapid prototyping of new methods for modelling and simulation of flow in porous media. Has a large user community from all over the world.
MRST-co2lab offers a set of open-source simulators and workflow tools that have been specially designed for the study of long-term, large-scale storage of CO2.
The Open Porous Media (OPM) initiative provides open-source software for simulation, upscaling and visualization of porous media processes, in particular subsurface reservoirs.
Open Quantum Computing is an open-source framework for conducting research on algorithms on noisy intermediate-scale quantum (NISQ) computers.
Software for static modeling and prediction of surface water and urban flooding based on analysis of topography/terrain.
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