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Proceedings of Workshops on Inverse Problems, Data, Mathematical Statistics and EcologyPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2011 (English)Conference proceedings (editor) (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Linköping: Linköping University Electronic Press , 2011. , p. 146
##### Series

LiTH-MAT-R, ISSN 0348-2960 ; 2011:11
##### National Category

Mathematics
##### Identifiers

URN: urn:nbn:se:liu:diva-69693ISRN: LiTH-MAT-R–2011/11–SEOAI: oai:DiVA.org:liu-69693DiVA, id: diva2:431256
#####

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PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt1178",{id:"formSmash:j_idt1178",widgetVar:"widget_formSmash_j_idt1178",multiple:true}); Available from: 2011-07-20 Created: 2011-07-18 Last updated: 2014-09-29Bibliographically approved

Processes in Nature may be considered as deterministic or/and random. We are observing global problems such as climate changes (e.g. warming and extreme weather conditions), pollutions (e.g. acidification, fertilization, the spread of many types of pollutants through air and water) and whole ecosystems that are under pressure (e.g. the Baltic sea and the Arctic region). To understand the processes in Nature and (predict) understand what might occur it is not enough with empirical studies. One needs theoretical fundaments including models and theories to perform correct actions against different threats or at least to carry out appropriate simulation studies. For example, extreme value theory can explain some of the observed phenomena, classical risk analysis may be of help, different types of multivariate and high-dimensional analysis can explain data, time series analysis is essential, for forthcoming studies the theory of experimental designs is of interest, data assimilation together with inverse problem technique is useful for adjustment of data into mathematical models and the list can be made much longer. Behind all these approaches mathematics is hidden, sometimes at a very advanced level. Chemical and physical processes influence all observations but the challenge is to do appropriate approximations so that mathematical/statistical models can be applied. The main aim of this project is to present state of the art knowledge concerning the modelling of Nature with focus on mathematical modelling, in particular "inverse and ill-posed problems", as well as spatiotemporal models. Inverse and ill-posed problems are characterized by the property that the solutions are extremely sensitive to measurement and modelling errors. There are established connections between inverse problems and Bayesian inference but very little has been carried out with focus on parametric inference such as the likelihood approach. Concerning spatio-temporal models these are usually extensions of classical time series models or/and classical multivariate analysis models.

From the Nordic Council of Ministers, within the program Nordic - Russian Cooperation in Education and Research we asked for funding of 3 preparatory meetings where the plan was to create a series of events taking place during 2011-2013. Partner organizations were

- Institute of Problems of Mechanical Engineering, St. Petersburg
- St. Petersburg State University
- Helsinki University
- Swedish Agricultural University
- Stockholm University
- Linköping University

However, there were also some other participants from other universities.

The planned events should be connected to the following fields: applied mathematics, biophysics and mathematical statistics. Within applied mathematics: mathematical modelling and partial differential equations, inverse and ill-pose problems, data assimilation, dynamical systems, linear algebra, matrix theory; within biophysics; neural networks and inverse modelling of objects; within mathematical statistical; analyses of stochastic processes, spatio-temporal modelling, experimental design, where considered. There exists a wide overlap between these areas and it is challenging to systemize this overlap and transmit this knowledge to students and stakeholders. However, due to unsure funding it was decided to discuss what can be presented during a one-year program. Moreover, due to practical reasons only 2 meetings/workshops were held:

- Workshop on Inverse Problems, Data, Mathematical Statistics and Ecology: May 20-21, 2010 at Department of Mathematics, Linköping University.
- Workshop on Inverse Problems, Data, Mathematical Statistics and Ecology, Part II: August 25-27, 2010 at Department of Mathematics, Helsinki University.

The output from the above events can be summarized as follows:

- We have identified a number of different areas which can be taught on from different perspectives depending on students background of mathematics.
- We have learned to know many interesting researchers who are willing to share there experiences when for example creating a summer school.
- There is no doubt that we can organize cross-disciplinary summer/winter schools with focus on either the Baltic or Archtic regions.

This booklet is also part of the deliverables. It comprizes extended abstracts of the majority of the talks of the participants showing their great interest. It is in some way a unique cross-disciplinary document which has joined researchers from different areas from Russia, Finland and Sweden.

We are extremely grateful for the support given by the Nordic Council of Ministers (NCM-RU-PA-2009/10382) and all the enthusiastic contributions by the participants, including our host in Helsinki, professor Lassi Päivärinta.

Vladimir Kozlo, Linköping University

Martin Ohlso, Linköping University

Dietrich von Rosen, Linköping University/SLU

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