Staff
Matthew Schrecker

Department of Mathematical Sciences

Dr Matthew Schrecker

Much of my research to date has focused on the compressible Euler equations. The Euler equations have a rich mathematical structure underpinning one of the most basic physical processes in the universe: the flow of gases. Over the past 250 years, the equations have inspired developments in mathematical analysis, functional and harmonic analysis, dynamical systems and geometric analysis. The Euler equations are also relevant in understanding phenomena in the physical world ranging from the flow out of an exhaust pipe to the motion of galaxies and collapse of stars. In all of these physical processes, the formation of shock waves is a ubiquitous phenomenon (as seen physically, for example, in a sonic boom coming from a Concorde aircraft) that causes a drastic loss of regularity in solutions of the system. The presence of shocks has led to the need to study weak solutions of the Euler equations, as classical (differentiable) solutions will generically blow up in finite time, and so my research has focused on both of these aspects: the existence of weak solutions and the properties and behaviour of smooth solutions on approach to singularity formation.

More recently, I have also been working on the problem of the gravitational collapse of stars, a different type of singularity formation in which the physical variables of the system (particularly the density of a star) blow up in finite time as matter falls in towards the centre of the star. Such a phenomenon is also modelled with the Euler equations, but now with a gravitational field as well.

Research Interests:

  • Analysis of Partial Differential Equations
  • Fluid dynamics (especially free boundary problems and nonlinear singularity formation)
  • Shock waves (their formation, structure and dynamics)

LINKS:

Matthew Schrecker on the University of Bath Research Portal

Lead Supervisors

Eric Hester

  • Asymptotic analysis of PDEs
  • Spectral algorithms for numerical PDE solvers
  • Multiphase fluid mechanics
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Stephen Wilson

Department of Mathematical Sciences

  • Fluid mechanics, especially thin-film flows, rivulets and evaporating droplets.
  • Non-Newtonian fluid mechanics, especially liquid crystals and thixotropic fluids.
  • More generally, the use of a range of mathematical (namely asymptotic, analytical and numerical) methods to bring new insights into a wide range of “real world” problems.
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Michael Murray

Department of Mathematical Sciences

  • Optimization: implicit regularization, geometry of the loss landscape
  • Generalization: benign and tempered overfitting, phase transitions in performance with respect to compute and data
  • Understanding emerging paradigms in ML, e.g., in-context learning, transformers etc.
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Federico Cornalba

Department of Mathematical Sciences

  • Modelling of large-scale interacting particle systems
  • Analysis and numerics of stochastic PDEs of Fluctuating Hydrodynamics
  • Reinforcement Learning methods
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Avi Mayorcas

Department of Mathematical Sciences

  • Regularisation by noise in stochastic partial and ordinary differential equations
  • Stochastic quantisation of physical field theories
  • Game theory and mean field dynamics in macroeconomics and finance
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Christoforos Panagiotis

Department of Mathematical Sciences

  • Percolation and lattice spin models
  • Probability on groups
  • Self-avoiding walk
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Haiyan Zheng

Department of Mathematical Sciences

  • Adaptive designs in clinical trials
  • Bayesian data augmentation
  • Finite mixture distributions
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Andreas Kyprianou

Andreas was instrumental in the development of SAMBa and was Co-Director from its inception until the end of 2022. He has left the University of Bath to take up the role of Chair of Probability at the University of Warwick.

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Jennifer Tweedy

Department of Mathematical Sciences

  • Mathematical medicine
  • Fluid mechanics
  • Mathematical modelling
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Thomas Burnett

Department of Mathematical Sciences

Tom’s research interest is in medical statistics, with a particular focus on the design and analysis of adaptive clinical trials.

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