Apr 8, 2016 - Mini symposium at SIAM UQ in Lausanne

Learning Parameters from Data: Calibration, Inverse Problems, and Model
SIAM UQ mini symposium, Friday 9 April, EPFL, Lausanne.

Alex Diaz, Marco Iglesias and myself have organised a mini symposium at the SIAM UQ conference, which will be in Lausanne this year.

** Session Abstract**

Complex numerical models have
become so ubiquitous in science,
that nowadays the conclusions drawn
from their use influence critical
and expensive decisions in science,
engineering and public policy. The
predictive power of such models relies
on how well they are calibrated to
experimental data. The main aim of this
minisymposium is to bring together
experts in calibration and inverse
problem modelling. Relevant topics for
include: robust calibration techniques,
inverse problem methodologies,
Bayesian model updating, history
matching, data assimilation, model
discrepancy, amongst others. Theoretical
and computational developments are
of interest, with particular focus on
numerical efficiency.
Organizer: Alejand

Faced with situations that we wish to improve, we need to formulate some kind of model
of how our controllable inputs to the situation affect the outputs. Whether we are selecting
the best inputs to result in the kids going to bed on time, or making a quiet aircraft engine,
modelling the situation is important. Models may be analytical, physics-based or empirical,
or a mixture. While analytical and physics-based models can be fast to run, they may not
be able to predict true-life outcomes. Physical experiments are often expensive and
display all the negatives/excitement of true-life - primarily uncertainty. Statistical modelling
methods are useful for combining data of varying lineage into a useful model that can be
used to predict outcomes, and so optimise situations. In this talk I will show the
development of such a model. It is a musculoskeletal/hydrodynamic model of the
underwater fly-kick of an Olympic swimmer, based on experimental, physics-based and
analytical data. Statistical model based optimization techniques are then used to develop
swimming techniques for optimal trade-offs between power and thrust. The techniques are
applicable in all areas of science where inputs can be modified to affect outputs which are
to be improved.

The speakers are

  • Alejandro Diaz (University of Liverpool)- History matching with structural reliability methods
  • Ian Vernon (Durham University) - Iterative History Matching for Computationally Intensive Inverse Problems
  • James Hensman (University of Lancaster) - Efficient Inverse problems with Gaussian process surrogates via entropy search
  • Zaid Sawlan (KAUST) - Bayesian inference for linear parabolic PDEs with noisy boundary conditions