SI2-CHE: ExTASY: Extensible Tools for Advanced Sampling and analYsis
Lead Research Organisation:
Imperial College London
Department Name: Computing
Abstract
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Organisations
People |
ORCID iD |
Panos Parpas (Principal Investigator) |
Publications
Tavares G
(2013)
On the information-based complexity of stochastic programming
in Operations Research Letters
Parpas P
(2013)
A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control
in Automatica
Ho C
(2014)
Singularly Perturbed Markov Decision Processes: A Multiresolution Algorithm
in SIAM Journal on Control and Optimization
Wright R
(2014)
Adaptive water distribution networks with dynamically reconfigurable topology
in Journal of Hydroinformatics
Parpas P
(2014)
A stochastic multiscale model for electricity generation capacity expansion
in European Journal of Operational Research
Wright R
(2014)
Dynamic Topology in Water Distribution Networks
in Procedia Engineering
Parpas P
(2015)
Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach
in INFORMS Journal on Computing
Wright R
(2015)
Control of water distribution networks with dynamic DMA topology using strictly feasible sequential convex programming
in Water Resources Research
Menke R
(2015)
Approximation of System Components for Pump Scheduling Optimisation
in Procedia Engineering
Description | The first objective of the project is to define the representational structures that will facilitate rigorous quantitative multiscale models to be developed. Our second objective is to develop algorithmic techniques for simulating, optimising and controlling stochastic multiscale systems. The project has delivered two major results so far. Firstly, we showed that the major benefit of dimensionality reduction techniques is that the reduced order model is numerically well behaved. This was a surprising result since intuition would suggest that the reduced order model can be solved more efficiently because it has fewer degrees of freedom. However we showed that this is not necessarily the case. This result has shed new light on the role of reduced order models and gave us several ideas about how to develop new algorithms based on this insight. Our second main result concerns an exact mathematical estimation of error bounds for stochastic models that capture multiscale dynamics across both time and space. Using modern optimisation techniques we were able to show rigorous bounds for spatial models with singular perturbations across space. It is important to note that while some of these models have been studied for over 30 years, the magnitude of the error due to the dimensionality reduction was (until now) impossible to estimate. Based on the results we have obtained so far we are currently working on the development of new algorithms. Since one of our objectives is to apply our tools to realistic case studies we have also started collecting and experimenting with real world data. |
Exploitation Route | The output of our research is software that can be used by people interested in molecular dynamic simulations. We are in the process of integrating our s/w into a package that will be made available to the wider community. |
Sectors | Agriculture, Food and Drink,Chemicals,Creative Economy,Education,Energy,Environment,Financial Services, and Management Consultancy,Pharmaceuticals and Medical Biotechnology |
Description | EU FP7 Marie Curie CIG |
Amount | £80,000 (GBP) |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 10/2012 |
End | 10/2016 |