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Faculty of Mathematics

 

Professor Rich Kerswell

Career

  • 2017- date:  DAMTP,  Cambridge University
  • 1996-2017:  Mathematics,  Bristol University
  • 1992-1996:  Mathematics,  Newcastle University
  • 1992          :  PhD,  MIT

Research

General fluid mechanics: the Navier-Stokes equations (nonlinear dynamics, transition and turbulence); geophysical and astrophysical fluid mechanics (e.g. convection; stably stratified flows; tidal, precessional and librational motion of planetary interiors and subsurface oceans; accretion disks);

Selected Publications

Publications

Recurrent flow patterns as a basis for two-dimensional turbulence: Predicting statistics from structures.
J Page, P Norgaard, MP Brenner, RR Kerswell
– Proceedings of the National Academy of Sciences of the United States of America
(2024)
121,
e2320007121
(doi: 10.1073/pnas.2320007121)
Godunov Loss Functions for Modelling of Hyperbolic Conservation Laws
RG Cassia, RR Kerswell
(2024)
(link to publication)
Long-wave instabilities of sloping stratified exchange flows
L Zhu, A Atoufi, A Lefauve, RR Kerswell, PF Linden
– Journal of Fluid Mechanics
(2024)
983,
a12
(doi: 10.1017/jfm.2024.96)
Predicting burst events in a forced 2D flow: A wavelet-based analysis
A Madhusudanan, RR Kerswell
(2024)
(link to publication)
The polymer diffusive instability in highly concentrated polymeric fluids
T Lewy, R Kerswell
– Journal of Non Newtonian Fluid Mechanics
(2024)
326,
105212
(doi: 10.1016/j.jnnfm.2024.105212)
Multistability of elasto-inertial two-dimensional channel flow
M Beneitez, J Page, Y Dubief, RR Kerswell
– Journal of Fluid Mechanics
(2024)
981,
a30
(doi: 10.1017/jfm.2024.50)
New insights into experimental stratified flows obtained through physics-informed neural networks
L Zhu, X Jiang, A Lefauve, RR Kerswell, PF Linden
– Journal of Fluid Mechanics
(2024)
981,
r1
(doi: 10.1017/jfm.2024.49)
Inertial enhancement of the polymer diffusive instability
R Kerswell
– Journal of Fluid Mechanics
(2024)
981,
a2
(doi: 10.1017/jfm.2024.21)
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning.
R Ohana, M McCabe, L Meyer, R Morel, F Agocs, M Beneitez, M Berger, B Burkhart, S Dalziel, D Fielding, D Fortunato, J Goldberg, K Hirashima, Y-F Jiang, R Kerswell, S Maddu, J Miller, P Mukhopadhyay, S Nixon, J Shen, R Watteaux, B Blancard, F Rozet, L Parker, M Cranmer et al.
– CoRR
(2024)
abs/2412.00568,
44989
(doi: 10.52202/079017-1430)
TheWell: a Large-Scale Collection of Diverse Physics Simulations forMachine Learning
R Ohana, M McCabe, L Meyer, R Morel, FJ Agocs, M Beneitez, M Berger, B Burkhart, SB Dalziel, DB Fielding, D Fortunato, JA Goldberg, K Hirashima, Y-F Jiang, RR Kerswell, S Maddu, J Miller, P Mukhopadhyay, SS Nixon, J Shen, R Watteaux, B Regaldo-Saint Blancard, F Rozet, LH Parker, M Cranmer et al.
– ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 37 (NEURIPS 2024)
(2024)
(link to publication)
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Research Group

High-Reynolds-Number Fluid Flow

Email

r.r.kerswell@damtp.cam.ac.uk

Room

G1.03

Telephone

01223 766349

Study at Cambridge

About the University

Research at Cambridge