Conservation laws as inductive biases

A basic pattern in nature is invariance: the notion that properties (e.g. energy) of a system remain unchanged through a transformation (e.g. time). However, learning such patterns from data can be challenging since they are often non-trivially disguised as variation in observed phenomena. The motivation for the work in the thesis is improved data efficiency when learning predictive models of physical dynamical systems. Building on ideas from machine learning and physics, it explores learning algorithms using conserved quantities and conservation laws as general purpose model components, with the aim of more efficient learning. Chapter 2 develops learning algorithms for task structured problems where the notion of a task is identified with an unobserved conserved quantity to be learned from data. The main contribution is a model that accounts for globally invariant sources of variation (e.g. the laws of physics) and task-specific sources of variation (e.g. system parameters). The idea is to encourage modularity: a separation of reusable components of the model from task-specific ones. The chapter empirically studies the model in the context of learning predictive models of dynamical systems. It is found to be useful as an inductive bias for modularity, as measured by data efficiency in multi-task, transfer- and meta-learning settings. Chapter 3 develops expressive function classes with inbuilt physical geometry such as conservation laws. The main contribution is a tying together of the theory of variational integrators and neural networks. This produces a scheme for deriving symplectic and momentum-preserving architectures (variational integrator networks). The architectures are studied empirically in the context of noisy, and image observations, of physical systems. In the former, they are found to be efficient and flexible learners. In the latter, they are found to learn physically meaningful geometric representations, enabling accurate long-term forecasts in image space. Learning modular task representations is potentially important for developing practically useful meta-learning algorithms. In chapter 2 the representations are non-hierarchical and require labelling at the task-level. Extending the idea to hierarchical and unsupervised settings is an interesting future direction. Physical geometry is an elegant example of compact general-purpose representations. Extending chapter 3 to different and more general physics, building on the literature on variational integrators, is also an interesting direction.Open Acces

Satellite Geological and Geophysical Remote Sensing of Iceland. Vatnajoekull Area, Iceland: New Volcanic and Structural Features on ERTS-1 Imagery

There are no author-identified significant results in this report

Wind effects on high rise buildings.

The wind effects on high rise buildings were studied by finding and reading books, articles and studying equations. This was done to calculate the first natural frequency of high rise buildings, wind induced acceleration on high rise buildings and how the comfort criteria of acceleration performing on high rise buildings acts on human bodies living in the building. The buildings that were studied are Turning Torso in Malmö, Sweden and briefly Smáratorg Tower in Kópavogur, Iceland. The information on the building Turning Torso is from 17. March 2000 (this is not the final dimension on the building it was later strengthened due to wind load). This information was then used to calculate the first natural frequency, wind induced acceleration and compared to the data that engineers at Turning Torso worked with. The most important results were that there will be excessive movement in the top floors of Turning Torso so that sensitive people may perceive motion and hanging objects may move. For Smáratorg Tower the movement is so excessive that majority of people perceive motion. The aim was to make a diploma work that can be used in practice, which can be a guide to design high rise buildings due to wind effects in the early states of development

Extremely periodic pulsating aurora observed near L=6: A new type pulsating aurora

Extremely periodic pulsating aurora, a new type pulsating aurora, was detected by three photometers (directing towards the zenith and 45° N and S in the meridian plane, for 427.8 nm emission) at Husafell in Iceland on 18-19 December 1985. We examined the characteristics of the pulsating auroras and their relationship to magnetic pulsations using the data obtained in Iceland and Syowa Station, the geomagnetically conjugate pair station in Antarctica. The characteristics of this event are as follows; 1) extremely regular periodic pulsating auroras with the frequency of -50 mHz were observed simultaneously on the 3 photometers, 2) the periodicity of the pulsation was extremely high, and the Q-value showed more than 20, 3) the intensity variation among the 3 photometers occurred with excellent coherency and simultaneously without time lag, suggesting that these pulsating auroras were not of a propagating type but a standing type, 4) there are no correlation between the optical pulsating auroras and magnetic pulsations on the ground. These characteristics suggest that the extremely periodic pulsating aurora on this event is not a common (popular) pulsating aurora but an exceptional type pulsating aurora which would occur under a certain condition in the magnetosphere

Zwei neue C14-Datierungen isländischer Vulkanausbrüche

Durch C14-Datierungen von lava-überdecktem Torf konnte das Alter von zwei west-isländischen Vulkanausbrüchen bestimmt werden. Hallmundarhraun (mit der Lavahöhle Surtshellir und den Wasserfällen Hraunfossar) ist ca. 1190 Jahre, Sydri-Raudamelsküla ca. 2615 Jahre alt.researc

Learning Contact Dynamics using Physically Structured Neural Networks

Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback—which is heavily relied upon by biological systems—is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations

Satellite geological and geophysical remote sensing of Iceland: Preliminary results of geologic, hydrologic, oceanographic, and agricultural studies with ERTS-1 imagery

The author has identified the following significant results. The wide variety of geological and geophysical phenomena which can be observed in Iceland, and particularly their very direct relation to the management of the country's natural resources, has provided great impetus to the use of ERTS-1 imagery to measure and map the dynamic natural phenomena in Iceland. MSS imagery is being used to study a large variety of geological and geophysical eruptive products, geologic structure, volcanic geomorphology, hydrologic, oceanographic, and agricultural phenomena of Iceland. Some of the preliminary results from this research projects are: (1) a large number of geological and volcanic features can be studied from ERTS-1 imagery, particularly imagery acquired at low sun angle, which had not previously been recognized; (2) under optimum conditions the ERTS-1 satellite can discern geothermal areas by their snow melt pattern or warm spring discharge into frozen lakes; (3) various maps at scales of 1:1 million and 1:500,000 can be updated and made more accurate with ERTS-1 imagery; (4) the correlation of water reserves with snowcover can improve the basis for planning electrical production in the management of water resources; (5) false-color composites (MSS) permitted the mapping of four types of vegetation: forested; grasslands, reclaimed, and cultivated areas, and the seasonal change of the vegetation, all of high value to rangeland management

The grey zones of technological innovation: negative unintended consequences as a counterbalance to novelty

Publisher's version (útgefin grein)The purpose of this article is to better understand the challenges of avoiding the dark side of technological innovation. Specifically, we analyse 10 public investigations started as a reaction to a major crisis in regenerative medicine at the Karolinska Institute, Sweden, associated with the clinician-scientist Paolo Macchiarini. We interpret the reaction as an attempt to restore the balance between the stimulation and regulation of technological innovation processes by clarifying ambiguities in the regulation at the interface between research and practice. We conceptualise these ambiguities as grey zones–situations when it is unclear if the benefits of experimentation outweigh its risks–and propose that grey zones are continually created and resolved as actors in innovation governance systems counterbalance the generation of novelty and the risk of negative unintended consequences.This research was financed by the Swedish Research Council Distinguished Professor’s Programme, awarded to Professor McKelvey, on “Knowledge-intensive Entrepreneurial Ecosystems: Transforming society through knowledge, innovation and entrepreneurship”, VR DNR 2017-03360.This research was also financed by the Bank of Sweden Tercentenary Foundation (Riksbankensjubileumsfond) through the project “How Engineering Science Can Impact Industry in a Global World”, lead by Professor M. McKelvey (FSK15 1080 1). This project is part of a large research program “The Long Term Provision of Knowledge” financed jointly by the Bank of Sweden Tercentenary Foundation, Formas, Forte and the Swedish Research Council.Peer Reviewe