The multiscale/multiphysics nature of materials gives rise to extremely challenging and unique mathematical and algorithmic problems currently prohibiting uncertainty quantification and predictive materials modelling. For example, information loss during coarse graining; high-dimensionality and scalability of stochastic multiscale simulations and UQ; learning in models with a high dimensional parameter space; rare events; transferability of potentials in electronic structure, atomistic and molecular simulations. Materials of interest include, but are not limited to, metallic alloys, ceramics, polymers and composites, semiconductors, biological and geological media and meta-materials. The ability to predict the properties of these materials with confidence is paramount to a number of sectors. Currently there is a huge gap between state-of-the-art mathematical methods and the analysis tools routinely used by the materials community.

This activity is a partnership of the Warwick Centre for Predictive Modelling, the Isaac Newton Institute for Mathematical Sciences (INI), the Turing Gateway to Mathematics and the Knowledge Transfer Network.

The academic organisers include Prof. Nicholas Zabaras (University of Warwick) and Markos Katsoulakis (University of Massachusetts at Amherst). For further details please contact Jane Leeks at the TGM.

There are two new open research positions within the WCPM. For more info click on: Research Fellow on Scientific Computing and Research Assistant on Scientific Computing.

Three fully funded PhD positions are available: Predictive Modelling of Chemomechanical Materials Failure Processes, supervised by Dr James Kermode; Uncertainty quantification for classical effective potentials, supervised by Dr Peter Brommer; Computational Science and Engineering, supervised by Prof. Nicholas Zabaras.