Summary
Complex systems research has been on the forefront of scientific priorities of many national research councils and the EU
for more than a decade. Many very interesting phenomena have been identified and explored, but the development of the
underpinning mathematical theory has been lagging behind. The proposed training network builds on an emerging
development in applied mathematics to provide proper mathematical theory for the existence of early-warning signals for
sudden changes in dynamical behaviour, so-called critical transitions, which have been reported by applied scientists in
various contexts.
Practical implications for the existence of such early-warning signals are far reaching, since these would enable the
development of better control strategies to avoid or diminish the effect of catastrophes. Topical examples include epileptic
seizures, stock market collapses, earthquakes, and climate.
Attending to the mathematical underpinning for critical transitions in complex systems, it is apparent that the relevant
mathematical discipline of bifurcation theory, that has been developed to great acclaim and use for primarily low-dimensional
deterministic autonomous (i.e. intrinsically time-independent) dynamical systems, and (in the context of phase transitions)
for material science, does not apply without nontrivial modification to most of the complex systems contexts.
The training network is a response to the needs of applied scientists (including many in the private sector) for a proper
mathematical underpinning of early-warning signals. After their training, the trained researchers will be at the very forefront
of this rapidly developing field, with many practical skills and crucial theoretical insights into the possibilities (and impossibilities) of early-warning signals for critical transitions in a wide range of contexts.
for more than a decade. Many very interesting phenomena have been identified and explored, but the development of the
underpinning mathematical theory has been lagging behind. The proposed training network builds on an emerging
development in applied mathematics to provide proper mathematical theory for the existence of early-warning signals for
sudden changes in dynamical behaviour, so-called critical transitions, which have been reported by applied scientists in
various contexts.
Practical implications for the existence of such early-warning signals are far reaching, since these would enable the
development of better control strategies to avoid or diminish the effect of catastrophes. Topical examples include epileptic
seizures, stock market collapses, earthquakes, and climate.
Attending to the mathematical underpinning for critical transitions in complex systems, it is apparent that the relevant
mathematical discipline of bifurcation theory, that has been developed to great acclaim and use for primarily low-dimensional
deterministic autonomous (i.e. intrinsically time-independent) dynamical systems, and (in the context of phase transitions)
for material science, does not apply without nontrivial modification to most of the complex systems contexts.
The training network is a response to the needs of applied scientists (including many in the private sector) for a proper
mathematical underpinning of early-warning signals. After their training, the trained researchers will be at the very forefront
of this rapidly developing field, with many practical skills and crucial theoretical insights into the possibilities (and impossibilities) of early-warning signals for critical transitions in a wide range of contexts.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/643073 |
| Start date: | 01-04-2015 |
| End date: | 31-03-2019 |
| Total budget - Public funding: | 3 896 688,10 Euro - 3 896 688,00 Euro |
Cordis data
Original description
Complex systems research has been on the forefront of scientific priorities of many national research councils and the EUfor more than a decade. Many very interesting phenomena have been identified and explored, but the development of the
underpinning mathematical theory has been lagging behind. The proposed training network builds on an emerging
development in applied mathematics to provide proper mathematical theory for the existence of early-warning signals for
sudden changes in dynamical behaviour, so-called critical transitions, which have been reported by applied scientists in
various contexts.
Practical implications for the existence of such early-warning signals are far reaching, since these would enable the
development of better control strategies to avoid or diminish the effect of catastrophes. Topical examples include epileptic
seizures, stock market collapses, earthquakes, and climate.
Attending to the mathematical underpinning for critical transitions in complex systems, it is apparent that the relevant
mathematical discipline of bifurcation theory, that has been developed to great acclaim and use for primarily low-dimensional
deterministic autonomous (i.e. intrinsically time-independent) dynamical systems, and (in the context of phase transitions)
for material science, does not apply without nontrivial modification to most of the complex systems contexts.
The training network is a response to the needs of applied scientists (including many in the private sector) for a proper
mathematical underpinning of early-warning signals. After their training, the trained researchers will be at the very forefront
of this rapidly developing field, with many practical skills and crucial theoretical insights into the possibilities (and impossibilities) of early-warning signals for critical transitions in a wide range of contexts.
Status
CLOSEDCall topic
MSCA-ITN-2014-ETNUpdate Date
28-04-2024
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Organisations
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| IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE (Coördinator)
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| FRIEDRICH-SCHILLER-UNIVERSITAT JENA
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| FUNDACION CIDAUT
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| KOBENHAVNS UNIVERSITET
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| TECHNISCHE UNIVERSITAET DRESDEN
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| THE UNIVERSITY OF EXETER
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| UNIVERSIDAD DE VALLADOLID
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| UNIVERSITEIT UTRECHT
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| UNIVERSITY COLLEGE CORK - NATIONAL UNIVERSITY OF IRELAND, CORK
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| WAGENINGEN UNIVERSITY