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STOCHASTICA - CA24104 - Stochastic Differential Equations: Computation, Inference, Applications

Stochastic Differential Equations: Computation, Inference, ApplicationsCOST Action CA24104

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Memorandum of Understanding (MoU)

The MoU explains in detail the Action's networking objectives, scientific tasks, and organisational structure. It is available for download as a PDF from the cost.eu website at the link below.

 

Join a Working group

To participate in the Action as a researcher or stakeholder, apply to a working group (WG). All WG applications will be processed within 3 months.

1. SDEs: theory and numerical methods

SDEs: theory and numerical methods will focus on the qualitative understanding of nonlinear SDEs with finite-dimensional state vectors, including stochastic systems with functional and delay coefficients, and the development of novel, efficient numerical methods for which there is still no well-developed theory of convergence and asymptotic analysis. The same classes of nonlinearities that disrupt the performance of standard numerical methods can be found in many different applications. For example, Chemical Langevin Equations, which characterise chemical reaction dynamics as continuous-time stochastic processes, Cox-Ingersoll-Ross (CIR), used as a stochastic model of volatility in finance, and epidemiological models, may all display coefficients with square-root dependence on the state. A breakthrough in the simulation of one would lead to efficiencies for all. 

WG leader: Prof John Appleby. 

 

2. SPDEs: theory and numerical methods

SPDEs: theory and numerical methods will extend the current state-of- the-art of high-performing and practical numerical procedures for SPDEs towards that which holds for deterministic PDEs. Many available results are for specific SPDEs on idealised domains and do not capture an appropriate level of complexity for applications. Emphasis will be placed on nonlinear SPDEs with potential terms and additive noise (relevant for inference and machine learning applications), and (semi-)linear SPDEs with multiplicative noise (applications include models of turbulent flow). 

WG leader: Dr Sonja Cox. 

 

3. Stochastic systems and statistical methods

Stochastic systems and statistical methods will work to understand the interplay between computation based statistical inference and stochastic numerical methods for SDEs defined in the broad sense, including those falling under the remit of WG1 and WG2. The overarching goal of the WG will be to develop a framework by which detailed recordings of the behaviour of an individual or several interacting individuals can be linked to a physically and mathematically appropriate description of the stochastic dynamics in terms of a system of SDEs. 

WG leader: Prof Mireille Bossy. 

4. Toolbox development and intersectoral coordination

Toolbox development and intersectoral coordination will organise stakeholder consultation, manage interaction with industry partners including recruitment to the Action, and serve as a single point of contact for external stakeholders. From the second year of the Action it will manage the development of a computational toolbox for industry and academic non-specialist expert modellers.  

WG leader and industry/stakeholder contact: Prof Gabriel Lord. 

5. Dissemination, Inclusion, Capacity Building, and Communication

Dissemination, Inclusion, Capacity Building, and Communication will be responsible for channels of communication between the Action and stakeholders, including the website and social media channels. It will manage engagement and outreach activities, including participation in public events, coordinate a mentoring programme for young researchers, and support the organisation of Training Schools associated with the domain of each of WG1-3 for capacity building.

WG leader: Prof Charles-Edouard Bréhier. 

STOCHASTICA

CA24104 - Stochastic Differential Equations: Computation, Inference, Applications

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