About the project
Objective
This project aims to develop a reliable and robust AI tool for early fault detection in power transformers. By combining physical laws with limited operational data, the project aims to predict the health and remaining lifetime of power transformers efficiently. The developed AI tool would enable proactive maintenance, reducing costs and enhancing power grid reliability.
Background
Power transformers play a silent but crucial role in our daily lives. They enable the transmission of electricity over long distances, powering homes, hospitals, industries, and public infrastructure. Although they usually operate unnoticed, transformers are subject to high stress and age over time. When a transformer fails, the consequences can be severe, including power outages, costly repairs, and safety risks. Today, many transformer failures happen because problems are detected too late, mainly due to limited and incomplete monitoring data.
To reduce these risks, various indicators are used, such as gas measurements, thermal images, vibration signals, and magnetic behavior, to assess a transformer’s condition. However, collecting large amounts of high-quality data from all these sources is difficult and expensive. Most existing AI methods depend heavily on large datasets, which limits their usefulness in real-world power systems. The project tackles this challenge by developing an AI model that combines data with physical knowledge about how transformers work. By embedding physics directly into the learning process, the model can learn meaningful patterns even from limited data.
The proposed approach uses a physics-enhanced multimodal neural operator framework that can combine information from multiple data sources and predict how long a transformer can continue to operate safely. The model is designed to be fast, reliable, and suitable for real-time monitoring. By enabling early fault detection and maintenance decisions, this research supports the digital transformation of power infrastructure and contributes to a more stable, efficient, and sustainable energy grid.
About the Digital Futures Postdoc Fellow
Abhishek Chandra is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. He holds a PhD in Electrical Engineering from Eindhoven University of Technology, The Netherlands, where his research focused on developing AI tools for characterizing piezoelectric and magnetic materials. His academic background includes applied mathematics, scientific computing, and AI. Abhishek has received several prestigious fellowships and scholarships, including the Digital Futures Postdoctoral Fellowship at KTH and the Information and Knowledge Society Scholarship at Université de Lille. With expertise in scientific machine learning and energy systems, his work aims to bridge the gap between theoretical AI models and practical engineering applications in critical infrastructure.
Main supervisor
Prof. Dr. Lina Bertling Tjernberg, Full Professor, Department of Electric Power and Energy Systems, EECS, KTH.
Co-supervisor
Prof. Dr. Cristian Rojas, Full Professor, Department of Decision and Control Systems, EECS, KTH.
About the project
Objective
This project aims to develop a DT-based risk-informed decision support methodology, specifically addressing the risk issue associated with stability and manoeuvrability loss of a scaled wind-assisted propulsion ship system. By integrating the DTs into Dynamic Risk Analysis (DRA) methodology, real-time risk estimation can be achieved, enabling proactive risk identification and control. Building on these real-time risk insights, a risk-informed decision support model will be trained to optimise sail attack angle adjustments, ensuring the scaled ship operates within a low-risk state.
Background
Digitalisation and decarbonisation are identified as the key transformative forces shaping the future of shipping, driving the adoption of marine green technologies. Therein, wind-assisted propulsion ship is recognised as one of the most promising solutions for green shipping. However, the deployment of large-scale sails in the wind-assisted propulsion system (WAPS) significantly alters ship’s weight and load distribution, increasing the inherent risk of stability and manoeuvrability loss, which threatens crew lives, assets and the marine environment.
Additionally, the continuous adjustments of sail direction to adapt to changing wind conditions during navigation introduce further uncertainties, amplifying the fluctuations in risk and highlighting the challenges in managing the dynamic risk levels associated with these systems. Therefore, achieving resilient and smart green shipping, which involves risk-interactive and adaptive automated operations, requires real-time, risk-informed decision support integrated with high-fidelity digitalisation. In this context, Digital Twins (DTs), by integrating the virtual and physical worlds, enable the real-time monitoring of operational scenarios and timely data analysis to head off issues before they arise, which holds great potential to enhance the risk-informed decision support yet still remains largely untapped.
About the Digital Futures Postdoc Fellow
Yue Han holds a Ph.D. in Design and Manufacture of Ship and Ocean Structure from Dalian University of Technology in China, where she specialised in the development of risk assessment methodologies and frameworks. With 7 years of research experience in this field, she has been working on dynamic risk analysis and intelligent decision-making of risk control strategies for marine structures such as ships, offshore installations, and their equipment. Now Yue is working on addressing the new hazards and failure mechanisms emerging from the recent implementation of green technologies and digital technologies in the maritime industry.
Main supervisor
Abbas Dashtimanesh
Co-supervisor
Jelena Zdravkovic and Giuseppe Belgioioso
About the project
Objective
Develop a multiscale modelling framework that combines experimental imaging, high-resolution simulations, and machine learning to accurately and efficiently predict fluid transport in fuel cell porous layers, enhancing our understanding of the multiscale physics and enabling improved system-level design and performance optimization.
Background
The global shift toward sustainable energy requires rapid development of efficient hydrogen technologies, with fuel cells playing a central role. A major challenge is that key fuel cell components, such as porous transport layers, involve complex multiscale fluid and transport processes that are difficult to characterize and model. Advancing predictive modelling tools for these components can significantly accelerate innovation and strengthen Sweden’s emerging hydrogen value chain. This project addresses this need by developing a modern, physically grounded, data-driven modelling foundation that can support improved design of fuel cell systems and inspire broader interdisciplinary research in electrochemical energy technologies.
Crossdisciplinary collaboration
Collaboration between the FLOW group in the Engineering Mechanics department and the Applied Electrochemistry group in the Chemical Engineering department at KTH Royal Institute of Technology.
About the project
Objective
- Develop a high-fidelity simulator that replicates Swedish railway operations, including infrastructure, rolling stock, power supply, signalling, and realistic operating behaviour.
- Create and analyse operational scenarios using historical, live, or simulated data to support performance monitoring, strategic planning, and subsystem interactions.
- Design analysis and visualisation modules to evaluate railway performance, including delay propagation, energy use, track capacity, driver behaviour, and the impact of digital technologies (e.g., ATO, DAC, VC).
- Demonstrate use cases with external partners through performance analysis, disruption management, decision support, subsystem compatibility testing, and development of a scalable digital twin architecture.
Background
Railways are essential for achieving climate-neutral, energy-efficient, and resilient mobility. In Sweden, they are a key pillar of sustainable transport policy. However, increasing capacity demands, operational complexity, infrastructure ageing, and the need for digital transformation pose major challenges. Despite progress in technologies and predictive management, the sector still lacks integrated platforms for testing, real-time data use, and efficient planning.
The SPOT-Rail project addresses these gaps by developing a cross-system railway demonstrator that replicates Swedish rail operations. At its core is a high-fidelity train driving simulator connected to live and historical data, supporting research, education, and strategic planning. This enables the testing of new technologies, evaluation of operational strategies, and development of decision-support tools.
By bridging research and real-world operations, SPOT-Rail promotes safer, more efficient, and environmentally friendly rail transport. It contributes to Sweden’s and Europe’s green transition goals while fostering innovation, collaboration, and a skilled workforce for the future of sustainable mobility.
Crossdisciplinary collaboration
The project brings together expertise from multiple disciplines to enable efficient and realistic simulation of railway operations. Key fields involved include: Railway systems engineering and train operations; Human factors and interaction design; Data science and artificial intelligence; Systems engineering. These disciplines contribute various theories, simulation modelling approaches, and machine learning algorithms for predictive analysis and decision support. Furthermore, the project aims to engage students by integrating its outcomes into university courses and offering opportunities for course projects and final theses. This strengthens the link between research, education, and practical application.
About the project
Objective
This project aims to study a dramatically different approach to software development of safety-critical systems. Specifically, the overall research goal is to: develop a new foundation for agile development of complex and regulated safety-critical cyber-physical systems that enable high-confidence rapid software development of systems with certification compliance requirements.
More specifically, we will address the following research challenges by:
- designing and evaluating a new agile development methodology for software and systems design for heavily regulated domains (specifically the defense industry), posing requirements on consistency between software implementation, design documentation, and compliance control according to certification authorities.
- developing new techniques, algorithms, and methods for supporting such new agile methodology, by designing new transformer-based optimization and verification techniques that are both sound and minimize false positives.
- constructing an interactive software prototype that can, in real-time, analyze software code and documentation, automatically perform compliance checks, and report live information on a dashboard available to the R&D organization.
Sweden’s defense industry’s competitiveness is vital for the safety and security of the country, where Saab AB is the major player. Two key components for competitiveness are development speed (short lead time) and flexibility (quickly adapting to changes). Both speed and flexibility are hampered by rigid processes: this project innovates in a dramatically different approach compared to current practices.
Background
Safety-critical cyber-physical systems—such as the modern aircraft fighters like Saab Gripen—are significantly relaying on software technology. Besides strong requirements of correctness and reliability, developing such systems falls under heavy regulation and certification control, including certification standards such as DO-178C. As a consequence, the development processes of such systems are extremely complex, requiring significant manual documentation, formal meetings, and control, which result in long development, innovation, and release cycles.
On the other hand, the development of non-safety critical software has, for several decades, been using agile methodologies (e.g., Scrum and Kanban) and quick iteration cycles. Moreover, the recent trend with generative AI tools based on LLMs and transformer technology, has paved the way for even more rapid development, using AI assisted pair programming systems such as GitHub Co-pilot, CodeWhisperer, and Codeium.
The key question addressed in this project is: how can agile development methodologies and assisting software tools be designed in the context of safety-critical systems with certification and regulation requirements? Specifically, the research problems concerns (i) soundness—how can we guarantee the correctness of analyses results, (ii) completeness—how can false positives be mitigated to make the system useful in practice, and (iii) explainability—how can analysis results be traced back to source data.
Crossdisciplinary collaboration
This project is conducted in close collaboration between the aerospace and defense company Saab AB and KTH Royal Institute of Technology.
Participating in the project:
- Main PI: David Broman, Professor, KTH Royal Institute of Technology
- Co-PI: Thomas Nordh, Product Owner and Business Area Leader, Saab AB
- Co-PI: Daniel Stensér, Digital Acceleration Officer, Saab AB
About the project
Objective
This project aims to align the legal requirements for emerging digital technologies, such as AI systems, with the development of such technologies. The regulatory demands for responsible AI, such as the need for transparency, protecting privacy and taking into account fundamental rights, are mandated by the recently enacted AI Act. This project will identify and address the challenges associated with transposing the legal demands of the AI Act into technical specifications in order that the developed technology is ethical and also legally compliant from the outset.
This industrial postdoc project is a collaborative initiative between KTH, Stockholm University, and industry partner Scania. By integrating legal and technical expertise, it will contribute to Scania’s broader efforts within AI regulatory compliance and support the implementation of responsible AI practices across its internal initiatives.
Background
The ICT sector has an environmental footprint. The future development of this footprint is debated, and it Artificial intelligence (AI) has many positive uses but the widespread adoption of this technology has also raised some concerns over the risks associated with its usage, such as biased decision-making, a lack of privacy preservation, environmental concerns and the diminishing of fundamental rights in general.
The European Union AI Act entered into force on the 1st of August 2024 and is the first comprehensive legal framework regulating AI systems. Its primary objective is to promote trustworthy AI systems, thereby ensuring that AI technologies are safe, transparent and aligned with EU values, while also fostering innovation.
This project will focus primarily on AI governance with the objective of promoting responsible AI as mandated by the AI Act. It will focus on how legal demands for transparency, trustworthiness, privacy, technical robustness, and avoidance of unfair bias should be interpreted for incorporation into corporate processes so as to proactively promote legal compliance. Part of this task will involve the identification and implementation of additional soft law elements, for example, harmonized standards developed by EU standardization bodies as well as guidelines produced by various EU institutions.
Partner Postdoc
Anna De Carvalho Guimarães
Main supervisor
Tobias Oechtering, KTH
Co-supervisor(s)
Stanley Greenstein, Stockholm University
Rami Mochaourab, Scania
About the project
Objective
This project aims to propose the generalized design method and computational architecture of more than 20 kinds of nonlinear functions. By this they can be used in more algorithm acceleration without doing a lot of repetitive development work.
Background
As the foundation of the future development of smart society, the digital chips play a key role. Behind these digital chips is the hardware acceleration of a large number of algorithms. The essence of the algorithm is mathematical operation, the most complex mathematical operation is nonlinear function calculation. So the proposed research question is how to improve the universality of nonlinear function VLSI design without affecting the performance and efficiency? It is a challenge.
About the Digital Futures Postdoc Fellow
Hui Chen received a PhD from Nanjing University (NJU) in 2022, China. His major is information and communication engineering. Hui’s research interests include arithmetic circuits, integrated circuits for elementary functions and reconfigurable computing.
Main supervisor
Zhonghai Lu, Professor at the Division of Electronics and Embedded Systems, Department of Electrical Engineering, EECS, KTH.
Co-supervisor
Masoumeh Ebrahimi, Associate Professor, Division of Electronics and Embedded Systems, Department of Electrical Engineering, EECS, KTH.