ERC FUNDED PROJECTS

GLOBEGOVE is a historical study of humanity’s relation to planetary conditions and constraints and how it has become understood as a governance issue. The key argument is that Global Environmental Governance (GEG), which has arisen in response to this issue, is inseparable from the rise of a planetary Earth systems science and a knowledge-informed understanding of global change that has affected broad communities of practice. The overarching objective is to provide a fundamentally new perspective on GEG that challenges both previous linear, progressivist narratives through incremental institutional work and the way contemporary history is written and understood. GLOBEGOVE will be implemented as an expressly global history along four Trajectories, which will ensure both transnational as well as transdisciplinary analysis of GEG as a major contemporary phenomenon. Trajectory I: Formation articulates a proto-history of GEG after 1945 when the concept of ‘the environment’ in its new integrative meaning was established and a slow formation of policy ideas and institutions could start. Trajectory II: The complicated turning of environmental research into governance investigates the relation between environmental science and environmental governance which GLOBEGOV examines as an open ended historical process. Why was it that high politics and diplomacy came in closer relations with environmental sciences? Trajectory III: Alternative agencies – governance through business and civic society explores corporate responses, including self-regulation through the concept of Corporate Social Responsibility, to growing concerns about environmental degradation and pollution, and business-science relations. Trajectory IV: Integrating Earth into History – scaling, mediating, remembering will turn to historiography itself and examine how concepts and ideas from the rising Earth system sciences have been influencing both GEG and the way we think historically about Earth and humanity.

2 500 000 €

Start date: 2018-10-01, End date: 2023-09-30

The objective with this proposal is to provide design tools and algorithms for model management in robust, adaptive and autonomous engineering systems. The increasing demands on reliable models for systems of ever greater complexity have pointed to several insufficiencies in today's techniques for model construction. The proposal addresses key areas where new ideas are required. Modeling a central issue in many scientific fields. System Identification is the term used in the Automatic Control Community for the area of building mathematical models of dynamical systems from observed input and output signals, but several other research communities work with the same problem under different names, such as (data-driven) learning. We have identified five specific themes where progress is both acutely needed and feasible: 1. Encounters with Convex Programming Techniques: How to capitalize on the remarkable recent progress in convex and semidefinite programming to obtain efficient, robust and reliable algorithmic solutions. 2. Fundamental Limitations: To develop and elucidate what are the limits of model accuracy, regardless of the modeling method. This can be seen as a theory rooted in the Cramer-Rao inequality in the spirit of invariance results and lower bounds characterizing, e.g., Information Theory. 3. Experiment Design and Reinforcement Techniques: Study how well tailored and ``cheap'' experiments can extract essential information about isolated model properties. Also study how such methods may relate to general reinforcement techniques. 4. Potentials of Non-parametric Models: How to incorporate and adjust techniques from adjacent research communities, e.g. concerning manifold learning and Gaussian Processes in machine learning. 5. Managing Structural Constraints: To develop structure preserving identification methods for networked and decentralized systems. We have ideas how to approach each of these themes, and initial attempts are promising.

2 500 000 €

Start date: 2011-01-01, End date: 2015-12-31

Modern society is critically dependent on large-scale networks for services such as energy supply, transportation and communications. The design and control of such networks is becoming increasingly complex, due to their growing size, heterogeneity and autonomy. A systematic theory and methodology for control of large-scale interconnected systems is therefore needed. In an ambitious effort towards this goal, this project will develop rigorous tools for control synthesis, adaptation and verification. Many large-scale systems exhibit properties that have not yet been systematically exploited by the control community. One such property is positive (or monotone) system dynamics. This correspond to the property that all states of a network respond in the same direction when the demand or supply is perturbed in some node. Scalable methods for control of positive systems are starting to be developed, but several fundamental questions remain: How can existing results be extended to scalable synthesis of dynamic controllers? Can results for linear positive systems be extended to nonlinear monotone ones? How about systems with resonances? The second focus area, adaptation, takes advantage of recent progress in machine learning, such as statistical concentration bounds and approximate dynamic programming. Adaptation is of fundamental importance for scalability, since high-fidelity models are very expensive to generate manually and hard to maintain. Thirdly, since systematic procedures for control synthesis generally rely on simplified models and idealized assumptions, we will also develop scalable methods to bound the effect of imperfections, such as nonlinearities, time-variations and parameter uncertainty that are not taken into account in the original design. The research will be carried out in interaction with industry studying a new concept for district heating networks. This collaboration will give access to experimental data from a full scale demonstration plant.

2 500 000 €

Start date: 2019-09-01, End date: 2024-08-31

This proposal envisages a research program in the field of systems and signals that will lead to innovative and agile mathematical modeling as well as model-based signal processing and control tools for applications in biology and medicine. The project's goal is to bridge the gap between systems biology, on one hand, and medical signal processing and control engineering, on the other. Mathematical models of systems biology will be used to devise algorithms for biological data processing and computerized medical interventions. Experimental biological and clinical data will be utilized to estimate and characterize the parameters of mathematical models derived for biological phenomena and mechanisms. An extensive collaboration network of medical researchers from Sweden and abroad will provide the project team with necessary experimental data as well as with access to medical competence. The envisaged tools are expected to be applicable more generally but their efficacy will be demonstrated in two main application areas. These areas are endocrinology and neurology for which the use of formal control engineering and signal processing methods is currently deemed to be most promising. The proposed program will result in novel systems and signals tools for medical research and health care enabling multi-input multi-output modeling and analysis of endocrine regulations and providing model-based algorithms for individualized drug dose titration.

2 379 000 €

Start date: 2010-04-01, End date: 2015-03-31