Skip to main content Skip to navigation

Warwick Centre for Predictive Modelling

Research

Methodological Themes

The Centre focuses on a number of fundamental themes common to many scientific applications, including:

  1. Mathematical and Statistical Foundations theme champion Susana Gomes
  2. Physics-based UQ and predictive modelling theme champion Tim Sullivan (aligned with the Alan Turing Institute's data-centric engineering programme)

Application Themes

Application themes at WCPM are:

  2. Electronic Devices theme champion Neophytous Neophytou (Energy GRP)
  3. Environmental Sustainability theme champion Souroush Abolfathi (Sustainable Cities GRP, Food GRP)
  4. Biological systems theme champion Mike Chappell (Health GRP)

Objectives of WCPM

WCPM is an interdisciplinary centre addressing the mathematical, statistical and scientific computing challenges necessary for predictive modelling in science and engineering. Our fundamental approach is in exploring synergies between Uncertainty Quantification (UQ), Machine Learning (ML) and Scientific Computing, in the fast-developing field becoming known as Scientific Machine Learning (SciML). The broad objectives of the Centre are:

1 To develop rigorous mathematical theory, algorithms and software to enable the quantification, analysis and subsequent control of complex multiscale systems in the presence of uncertainties, in a computationally scalable way (theme 1)
2 To demonstrate how a mathematical framework that addresses stochastic multiscale systems can be driven by limited and gappy information, leading to a new approach to capture and exploit uncertainty in engineered systems (theme 2)
3 To demonstrate the physical relevance and broad applicability of our multiscale framework through the consideration of a number of application themes (themes A-D above)

The emphasis of the Centre is on common themes linking the UQ, ML and Scientific Computing communities and identifying innovative research directions that can accelerate the impact of uncertainty modelling in engineering and the sciences as well as demonstrate the capabilities of computational uncertainty quantification methods and tools in various problems.