Staff
Eric Hester

Dr Eric Hester

Research interests:

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

Eric’s research develops accurate models and efficient simulations for multiphase systems in continuum mechanics. Applications span from optimising manufacture times in small-scale industrial microfluidics, through to assessing planetary-scale influence of ice-ocean interactions in global climate modelling. This research combines several techniques: developing software to automate asymptotic analysis of singularly perturbed partial differential equations, implementation of high performance numerical codes in the flexible and efficient Dedalus computational framework, as well as performing laboratory experiments of phase change phenomena. A unifying theme is how a sensible choice of “coordinates” (e.g. the elegant differential geometry of the signed-distance function for moving boundary layers, or the comprehensive algebra of Jacobi polynomials for sparse methods in numerical differential equations) can achieve rapid convergence, and thereby enable more efficient and explanatory models. The ultimate goal is to distill these methods into simple and generalisable predictions that help domain experts understand and optimise complex physical phenomena.

 

LINKS:

Eric Hester on the University of Bath research portal

Lead Supervisors

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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Matthew Schrecker

Department of Mathematical Sciences

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