About the project

Objective
This seed project aims to support the Digital Futures (DF) Digitalized Industry Working Group by strengthening the international research focus on industrial digitalization, driving sustainability and addressing related complexity from a systems thinking and engineering point of view. The overall objectives of this seed project are (1) to create a European network of leading research institutions, industry and other stakeholders and (2) to write a proposal for a suitable EU project to support this network. We primarily target an EU COST Action but will also consider other instruments.

Background
There are common challenges in sustainability in the manufacturing, transport, energy, water management, and building sectors, as well as related methodologies to deal with the associated socio-technical complexity. There is a fragmentation of competencies in the use of digitalization to support sustainable development and also related to systems thinking/engineering. With the targeted network, we intend to try to overcome this fragmentation.

Cross-disciplinary collaboration
The PI and co-PIs are spread over two different KTH schools. Each PI needs international collaboration as we aim to apply for a proposal to build an EU network. The partners in the ongoing proposal involve eleven countries so far, >50% COSTInclusiveness Target Countries. We have had one in-person meeting at KTH on 12-13 August 2024 (see photo) and plan for the next meeting in person on 14 October 2024. Several online meetings have taken place since the start of the project.

Contacts: Ellen Bergseth, Martin Törngren, Yongkuk Jeong

About the project

Objective
We have two key objectives:

To this end besides extensive literature surveys we will organize thematic workshops to find synergies among digital future researchers. Besides, we will bring together international experts in the area for a conference at Digital Futures.

Background
Biological systems are shaped by their evolutionary history and adaptation animals acquire throughout their life. This implies that even if two beings exhibit similar behavior the underlying physiological mechanism could be different and vice versa. ‘Degeneracy’ and diversity can be seen at every scale in biological systems: from gene expressions to behavior and can be quantitatively observed in heavy-tailed distribution of physiological parameters. Parameters that describe a biological system usually do not follow the classical Gaussian (or exponential family) distribution.

This biological reality is in stark contrast to how we build computational models of biological systems: Either take a data-driven or normative approach. Data-driven models rely on extensive data but typically we end up with an average model. Normative modeling on the other hand seeks to model biological systems assuming (based on data or some physical/chemical principles) that the system is ‘optimized’ in some sense. Both approaches ignore the fact that cohorts of biological systems do not follow normal distribution and each animal is optimized in their own way.

In this project we want to better understand the structure of the non-normal nature of biological data and how it can be better captured in our computational models.

Cross-disciplinary collaboration
We will focus on three specific spatial scales i.e. molecular level (i.e. genome, transcriptome and proteome), tissue/organ level (i.e. neural networks, cellular physiology, multiscale tissue mechanics, computational human models) and behavior level (i.e. decision-making, motor control). While the data at each level is different we expect there will be some common principles that relate diversity at different levels.

Contacts: Arvind Kumar, Xaiogai Li and Lanie Gutierrez-Farewik

About the project

Objective
This project aims to characterise the brain’s mechanical properties through Magnetic resonance elastography (MRE) in adolescents and older children and correlate them with risk factors for developing anxiety disorders. Our long-term goal is to characterize the brain’s mechanical properties at different ages to improve the diagnosis and treatment of anxiety disorders in the young population. Our long-term aim is to extend this project to characterize the mechanical properties of the brain at different stages of brain development, which can be used to improve the diagnoses of various neuropsychological diseases, especially at early stages where treatments are more likely to have an effect and to track the response of patients to treatments better.

Background
Very little is currently known about the evolution of the mechanical tissue properties of the brain in the first two decades of life. Such information can be valuable in improving the diagnosis of prevalent neuropsychiatric disorders in the young population, particularly anxiety disorders. MRE in the brain is a new technique in which mechanical properties of the brain tissue are estimated non-invasively. MRE has barely been used in children. MRE for the brain is only available in a few sites worldwide. This Spring, KTH will become the only site in Sweden with this technology. This opens a tremendous strategic opportunity for KTH to take the lead in its use for brain diseases.

Crossdisciplinary collaboration
The researchers in the team represent the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH and the Psychology Department at Stockholm University.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
This research project aims to realize faster-than-real-time (simulation time less than physical flow time) and high-resolution fluid flow simulation in engineering applications, with indoor climate as a pilot. The expected outcome of this project is a static CNN for super-resolution to achieve fast prediction of steady-state indoor airflow, a hybrid RCNN for super-resolution to achieve faster-than-real-time prediction of transient indoor airflow and standards for low-resolution input data by numerical simulation and experimental data. Besides the indoor flow simulations, this project would open a broad spectrum of engineering accurate computational fluid dynamics (CFD) applications complementary to today’s standard application of RANS turbulence models.

Background
Fast, high-resolution heat and mass transfer prediction are critical in many engineering applications. For example, in creating a desired indoor climate, fast and high-resolution airflow and contaminant transport simulation would help sustain life, reduce cost, and minimize energy consumption. In specific scenarios such as emergency management, conceptual design, heating, ventilation, air conditioning and refrigeration (HVAC&R) system control, faster-than-real-time simulation is desired.

Borrowed from computer vision and image recognition, super-resolution is a promising novel approach for the fast analysis of fluid mechanics problems. Super-resolution refers to techniques that obtain a high-resolution flow image output from a low-resolution flow image input. However, super-resolution implementation in real engineering applications faces two major challenges. Firstly, the input low-resolution flow data could be either obtained by fast and computationally inexpensive simulation, e.g., coarse grid CFD simulation, which might be associated with wrong physics or by experimental measurements, where the least number of sensors needed should be identified. Secondly, for transient indoor/outdoor airflow, super-resolution fails to extrapolate the flow fields that belong to a different statistical distribution. Therefore, this study aims to solve the two challenges that state-of-the-art super-resolution models face.

Crossdisciplinary collaboration
The researchers in the team represent the KTH of Architecture and the Built Environment, Department of Civil and Architectural Engineering, KTH School of Engineering Sciences, and Department of Engineering Mechanics.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
The project envisions a mobile cyber-physical system where people carrying mobile sensors (e.g., smartphones, smartcards) generate large amounts of trajectory data to sense and monitor human interactions with physical and social environments. The project aims to develop a causal artificial intelligence (AI) methodology to analyze and model human mobility behaviour dynamics (decision-making) using individual travel trajectory data and develop the causal diagrams of human mobility behaviour under disturbances that could help design effective strategies for sustainable and resilient urban mobility systems. The research challenges are learning the complex ‘hidden’ human decision-making mechanism from pervasive ‘observed’ trajectories and developing effective, scalable causal AI models and algorithms.

Background
The ever-changing mobility landscape and climate change continue challenging existing operating models and the responsiveness of city planners, policymakers, and regulators. City authorities have growing investment needs that require more focused operations and management strategies that align mobility portfolios to societal goals. The project targets the root cause of traffic (human) and novel analytic techniques to learn and predict human mobility behaviour dynamics from pervasive mobile sensing data that can help cities meet both sustainability challenges (through predicting congestion, emissions, and energy consumption) and improve urban resilience to disruptive events (such as infrastructure failures, natural disasters, or pandemics).

The human mobility area witnessed active developments in two broad but separate fields: transport and computer science. They work with different data, use different methods, and answer different but overlapping questions, i.e., mobility behaviour modelling using ‘small’ data in transport and mobility pattern analysis using ‘big’ data in computer science. A solid bridge between these is beneficial and needed but is still an open challenge. Mobile sensing and information technology have enabled us to collect much mobility trajectory data from human decision-makers. The predictive AI techniques show the potential to learn and predict human mobility using these trajectory data efficiently. However, they continually run up against the limits of what they observe (correlations, not causal relationships), thus hindering any serious applicability for preparedness and response policies for cities without understanding the causal mobility dynamics.

cAIMBER will bridge the two human mobility research streams in Transport Science and Computer Science. Also, it will develop the causal AI methodology, merging the RL and Causal Inference research fields. Integrating interdisciplinary expertise and techniques will derive generalizable insights about human behaviour dynamics that contribute to the scientific communities’ theoretical conceptualization of travel choices and decision-making mechanisms. Practically, cAIMBER conducts extensive empirical analysis using a comprehensive dataset covering different types of system disturbances for seven years. The accumulated knowledge of human mobility under these situational contexts would help city planners and service operators to make more informed decisions for sustainable and resilient travel.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Architecture and Built Environment (ABE), Civil and Architectural Engineering Department, Transport Planning Division and KTH School of Engineering Science (SCI), Mathematics Department, Mathematics for Data and AI Division. Strategic research partners at KTH iMobility Lab and MIT Transit Lab support the project.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
Digital humans and AI are becoming an integrated part of society. Modern chatbots such as GPT3 (Generative Pre-trained Transformer 3) have shown a remarkable capability of generating human-like responses and may even be prompted to act sarcastic, depressed or shy. To bring such systems into an embodied agent, such as a digital human, the agent’s body motion should reflect the same psychological inner state. This is, however, lacking in modern synthesis systems of non-verbal behaviours, which only generate generic motion based on a neutral speaker.

In this project, we propose to enhance virtual agents’ state of the art by giving them a psychological inner state that colours their nonverbal behaviour. We term these agents artificial actors – virtual digital humans that can take directions and produce expressive and convincing acting behaviour, much like a real actor takes instructions from a director. This means that it should be possible to instruct a character not only what to do or talk about but also how these actions should be performed (e.g. as a shy person with social phobia).

The project includes

We will specifically create a virtual agent simulating a therapy patient and evaluate its performance with therapists or therapists in training.

Background
To be announced

Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, KTH and the Psychology Department at Stockholm University.

About the project

Objective
This project aims to combine magnetic resonance elastography (MRE) and advanced diffusion magnetic resonance imaging (dMRI) to develop the next generation of MRE methods to improve the biomechanical characterization of brain tissue.

More specifically, A) we will propose computational models that consider the diffusion processes to estimate biomechanical properties of the brain at a sub-voxel level and B) we will use advanced dMRI data to predict biomechanical properties without the expensive equipment required by MRE.

We will apply these new methods to data we are acquiring from Parkinson’s disease (PD) and cancer patients and healthy subjects and test their potential to improve the diagnosis and treatment of patients and to characterize biomechanical changes during ageing.

Background
Neurological disorders affect millions of persons worldwide. While MRI is crucial in clinical diagnostics, it is largely limited to morphological features. Understanding how the mechanical properties of tissues change due to disease can give valuable information for improving their early detection and diagnosis. A promising tool for non-invasively estimating these mechanical properties through magnetic resonance imaging (MRI) is Magnetic Resonance Elastography (MRE). The current MRE methods generate limited mechanical information about the brain tissue at a relatively low image resolution. The proposed methods will provide a better mechanical characterization of tissues for improved diagnosis. Moreover, using additional MRI modalities, we can better understand the mechanism behind the changes in mechanical properties due to diseases. This can eventually lead to better treatments for patients. MRE for the brain is only available in a few sites worldwide. In Autumn 2022, the KTH-owned MRI scanner became the first one in Sweden capable of performing MRE examinations in the brain.

Crossdisciplinary collaboration
In this project, we are establishing a new cross-disciplinary and complementary collaboration: