Latent Force Models: Bridging the Divide between Mechanistic and Data Modelling Paradigms

15^th May 2012, 11:00 Ashton Lecture Theatre
Prof. Neil Lawrence
Department of Computer Science
University of Sheffield
UK

Abstract

Physics based approaches to data modeling involve
constructing an accurate mechanistic model of data,
often based on differential equations. Machine
learning and statistical approaches are typically data
driven---perhaps through regularized function
approximation.

These two approaches to data modeling are often seen
as polar opposites, but in reality they are two
different ends to a spectrum of approaches we might
take.

In this talk we introduce latent force models. Latent
force models are a new approach to data
representation that model data through unknown
forcing functions that drive differential equation
models. By treating the unknown forcing functions
with Gaussian process priors we can create
probabilistic models that exhibit particular physical
characteristics of interest, for example, in
dynamical systems resonance and inertia. This allows
us to perform a synthesis of the data driven and
physical modeling paradigms.