About:
In current decision making, it is usual to update a probabilistic model of the system using a mixture of data assimilation and inverse problems solutions. Then, a stochastic optimisation problem is framed in order to make decisions as the need arises. Heuristic methods are used for both data assimilation and stochastic optimisation. However, stochastic control and sequential decision theories indicate that this process is sub-optimal, but it is far from obvious how to do better. So, can the mathematical sciences provide anything to improve matters?
Within this context, there is recognition of the need to engage more widely across communities and bring together relevant researchers from applied mathematics, statistics and industry practitioners in order that they may work together to solve these problems.
Topics Covered:
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Emulation in statistics and surrogate methods in optimisation
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Optimisation for inverse problems and for applications optimisation
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Optimisation and inverse problems theory for uncertainty quantification
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Monte-Carlo ensemble and ensemble variation methods for uncertainty quantification
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System optimisation in the presence of uncertainty
Sponsors and Funders:
This workshop is a partnership of the Smith Institute, Isaac Newton Institute for Mathematical Sciences (INI), the Turing Gateway to Mathematics, the Knowledge Transfer Network, and the International Centre for Mathematical Science (ICMS).