Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

(Acido)bacterial diversity in space and time

Recent technological achievements enabled microbiologists to fully grasp the vast diversity of microbial life that is resident in soils, highly complex matrices of alternating micro-habitats on very small scales. Since then, microbial community composition has been catalogued for many different terrestrial habitats. This triggered the investigation and definition of processes which shape these communities. In most cases, the environment determines community composition, and similar habitats may feature similar microbial communities despite being far apart. However, some habitats have been described as subjected to pronounced neutral processes, which are dispersal, ecological drift or speciation. The balance between these process types is now the subject of many studies looking at microbial communities. It is also clear that these processes need to be monitored on both temporal and spatial scales, as the two dimensions are inseparably interlinked. However, most microbial studies deal with only one aspect, but do not control for the other. In this work, the outcome of a highly sophisticated plot scale experiment is presented encompassing 358 sampling locations distributed between six intra-annual sampling points on a 10 m x 10 m unfertilized grassland site in the Swabian Alb. RNA was extracted from the A-horizon of each soil and the hypervariable region 3 of the ribosomal small subunit was amplified and sequenced with barcoded Illumina sequencing. Roughly 400 million eubacterial reads were obtained. The dataset was used to assess the population dynamics of Acidobacteria, as well as the spatio-temporal co-occurenze of functionally depending microorganism. Additionally, preliminary results motivated the assessment of common methods for the examination of rhizospheric communities. In combination, the diversity of bacterial communities in space and time was tested from different angles, reflecting different research question, and they all revealed a far more complex reality than previously thought

15th Conference on Dynamical Systems Theory and Applications DSTA 2019 ABSTRACTS

From Preface: This is the fifteen time when the conference „Dynamical Systems – Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and the Ministry of Science and Higher Education. It is a great pleasure that our invitation has been accepted by so many people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcome nearly 255 persons from 47 countries all over the world. They decided to share the results of their research and many years experiences in the discipline of dynamical systems by submitting many very interesting papers. This booklet contains a collection of 338 abstracts, which have gained the acceptance of referees and have been qualified for publication in the conference edited books.Technical editor and cover design: Kaźmierczak, MarekCover design: Ogińska, Ewelina; Kaźmierczak, Mare