(Re-)learning to simulate: a look at the new science of data-driven computational modelling

Event


Date:

Event time : 6:00pm

Bayes Centre

Room G.03 (ground floor), 47 Potterrow, Edinburgh, EH8 9BT, UK

Organiser: Helen Stimpson

Organiser Email: ima.scotland1@gmail.com

Tuesday February 19, 2019 6:00pm Tuesday February 19, 2019 6:00pm Europe/London (Re-)learning to simulate: a look at the new science of data-driven computational modelling Bayes Centre, Room G.03 (ground floor), 47 Potterrow, Edinburgh, EH8 9BT, UK (Re-)learning to simulate: a look at the new science of data-driven computational modelling, a talk by Professor Ben Leimkuhler FIMA FRSE […]
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Event Link: https://ima.org.uk/10790/re-learning-to-simulate-a-look-at-the-new-science-of-data-driven-computational-modelling/
Helen Stimpson ima.scotland1@gmail.com

(Re-)learning to simulate: a look at the new science of data-driven computational modelling


(Re-)learning to simulate: a look at the new science of data-driven computational modelling, a talk by Professor Ben Leimkuhler FIMA FRSE (Professor of Applied Mathematics, University of Edinburgh)

Abstract

What is a mathematical model?  It is a representation, a condensation, a simplification.  For example, the solar system is reasonably modelled by Newton’s laws of motion.  From these equations, knowledge of the masses of the planets, and knowledge of the initial conditions, one can predict, to high accuracy, the positions of the planets for many millions of years.  In other applications, the formulation of a tractable model is much more difficult.  For example, we may not know the parameters of the model very well, and the model may be very sensitive to small changes in these.  We may not even know the underlying mathematical relationships that would define a good model.  Think of the stock market, or political elections.  Even when we have a good model, it might be not be useful if it requires excessive computation to find solutions.  Thus it is often difficult to represent complex systems and to make useful quantitive predictions.  In such cases it is necessary to find creative ways to explore the system of interest.  Increasingly one seeks to use data (either from observations or generated by simulations) to better understand complex systems.

In this talk I will discuss some of the challenges and opportunities of combining data, mathematical modelling, and scientific computing to address very challenging questions with potential importance for science, engineering and society.  By embedding complex problems (and data sets) in a physical modelling framework it is sometimes possible to find new ways to understand them.  I will discuss diverse examples ranging from molecular models to the analysis of wind farm performance to political gerrymandering.

No charge is made to attend meetings and non-members are welcome.  There is no requirement to book a place at this event.

Image credit: Edinburgh by barnyz / FlickrCC BY-NC-ND 2.0

 

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