SI2-CHE: ExTASY: Extensible Tools for Advanced Sampling and analYsis
Lead Research Organisation:
Imperial College London
Department Name: Computing
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
| Panos Parpas (Principal Investigator) |
Publications
Baltean-Lugojan R
(2016)
Robust Numerical Calibration for Implied Volatility Expansion Models
in SIAM Journal on Financial Mathematics
Borovykh A
(2021)
On stochastic mirror descent with interacting particles: Convergence properties and variance reduction
in Physica D: Nonlinear Phenomena
Campos J
(2019)
A multilevel analysis of the Lasserre hierarchy
in European Journal of Operational Research
Campos J
(2018)
A Multigrid Approach to SDP Relaxations of Sparse Polynomial Optimization Problems
in SIAM Journal on Optimization
Ho C
(2019)
Newton-type multilevel optimization method
in Optimization Methods and Software
Ho C
(2019)
Empirical risk minimization: probabilistic complexity and stepsize strategy
in Computational Optimization and Applications
Ho C
(2014)
Singularly Perturbed Markov Decision Processes: A Multiresolution Algorithm
in SIAM Journal on Control and Optimization
Hovhannisyan V
(2017)
Multilevel Approximate Robust Principal Component Analysis
Hovhannisyan V
(2016)
MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization
in SIAM Journal on Imaging Sciences
| 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 |