Imperial College London

Dr Chris Cantwell

Faculty of EngineeringDepartment of Aeronautics

Senior Lecturer in Aeronautics



+44 (0)20 7594 5050c.cantwell Website




Department of Aeronautics, Room 219City and Guilds BuildingSouth Kensington Campus





Publication Type

70 results found

Kim MY, Nesbitt J, Koutsoftidis S, Brook J, Pitcher D, Cantwell C, Handa B, Jenkins C, Houston C, Rothery S, Jothidasan A, Perkins J, Bristow P, Linton N, Drakakis E, Peters N, Chowdhury R, Kanagaratnam P, Ng FSet al., 2022, Immunohistochemical characteristics of local sites that trigger atrial arrhythmias in response to high frequency stimulation, EP Europace, ISSN: 1099-5129

Introduction: The response to high frequency stimulation (HFS) is used to locate putative sites of ganglionated plexuses (GPs), which are implicated in triggering atrial fibrillation (AF). Objective: To identify topological and immunohistochemical characteristics of presumed GP sites functionally identified by HFS. Methods: 63 atrial sites were tested with HFS in 4 Langendorff-perfused porcine hearts. A 3.5mm tip quadripolar ablation catheter was used to stimulate and deliver HFS to the left and right atrial epicardium, within the local atrial refractory period. Tissue samples from sites triggering atrial ectopy/AF (ET) sites and non-ET sites were stained with choline acetyl transferase (ChAT) and tyrosine hydroxylase (TH), for quantification of parasympathetic and sympathetic nerves, respectively. The average cross-sectional area (CSA) of nerves was also calculated.Results: Histomorphometry of 6 ET sites (9.5%) identified by HFS evoking at least a single atrial ectopic was compared with non-ET sites. All ET sites contained ChAT-immunoreactive (ChAT-IR) and/or TH-immunoreactive nerves (TH-IR). Nerve density was greater in ET sites compared to non-ET sites (nerves/cm2: 162.3 ±110.9 vs 69.65 ±72.48; p=0.047). Overall, TH-IR nerves had larger CSA than ChAT-IR nerves (µm2: 11,196 ± 35,141 vs 2,070 ± 5,841; p<0.0001), but in ET sites, TH-IR nerves were smaller than in non-ET sites (µm2: 6,021±14,586 vs 25,254 ± 61,499; p<0.001).Conclusions: ET sites identified by HFS contained higher density of smaller nerves than non-ET sites. Majority of these nerves were within the atrial myocardium. This has important clinical implications on devising an effective therapeutic strategy for targeting autonomic triggers of AF.

Journal article

Lino M, Fotiadis S, Bharath AA, Cantwell CDet al., 2022, Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics, PHYSICS OF FLUIDS, Vol: 34, ISSN: 1070-6631

Journal article

Coveney S, Cantwell C, Roney C, 2022, Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate, MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol: 60, Pages: 2463-2478, ISSN: 0140-0118

Journal article

Lindblad D, Sherwin S, Cantwell C, Lawrence J, Proenca A, Moragues Ginard Met al., 2022, Aeroacoustic analysis of a subsonic jet using the discontinuous Galerkin method, 28th AIAA/CEAS Aeroacoustics 2022 Conference, Publisher: American Institute of Aeronautics and Astronautics, Pages: 1-21

In this work, the open-source spectral/hp element framework Nektar++ is coupled with the Antares library to predict noise from a subsonic jet. Nektar++ uses the high-order discontinuous Galerkin method to solve the compressible Navier-Stokes equations on unstructured grids. Unresolved turbulent scales are modeled using an implicit Large Eddy Simulation approach. In this approach, the favourable dissipation properties of the discontinuous Galerkin method are used to remove the highest resolved wavenumbers from the solution. For time-integration, an implicit, matrix-free, Newton-Krylov method is used. To compute the far-field noise, Antares solves the Ffowcs Williams - Hawkings equation for a permeable integration surface in the time-domain using a source-time dominant algorithm. The simulation results are validated against experimental data obtained in the Doak Laboratory Flight Jet Rig, located at the University of Southampton.

Conference paper

Son O, Gao A, Gursul I, Cantwell C, Wang Z, Sherwin Set al., 2022, Leading-edge vortex dynamics on plunging airfoils and wings, Journal of Fluid Mechanics, Vol: 940, Pages: 1-30, ISSN: 0022-1120

The vortex dynamics of leading-edge vortices on plunging high-aspect-ratio (AR = 10) wings and airfoils were investigated by means of volumetric velocity measurements, numerical simulations, and stability analysis in order to understand the deformation of the leading-edge vortex filament and spanwise instabilities. The vortex filaments on both the wing and airfoilexhibit spanwise waves, but with different origins. The presence of a wing tip causes the leg of the vortex to remain attached to the wing upper surface, while the initial deformation of the filament near the wing-tip resembles a helical vortex. The essential features can be modelled as the deformation of initially L-shaped semi-infinite vortex column. In contrast, the instabilityof the vortices is well captured by the instability of counter-rotating vortex pairs, which are formed either by the trailing-edge vortices or the secondary vortices rolled-up from the wing surface. The wavelengths observed in the experiments and simulations are in agreement with the stability analysis of counter-rotating vortex pairs of unequal strength.

Journal article

Lino M, Fotiadis S, Bharath AA, Cantwell Cet al., 2022, Towards fast simulation of environmental fluid mechanics with multi-scale graph neural networks, AI for Earth and Space Science, Publisher: ICLR, Pages: 1-11

Numerical simulators are essential tools in the study of naturalfluid-systems, but their performance often limits application in practice.Recent machine-learning approaches have demonstrated their ability toaccelerate spatio-temporal predictions, although, with only moderate accuracyin comparison. Here we introduce MultiScaleGNN, a novel multi-scale graphneural network model for learning to infer unsteady continuum mechanics inproblems encompassing a range of length scales and complex boundary geometries.We demonstrate this method on advection problems and incompressible fluiddynamics, both fundamental phenomena in oceanic and atmospheric processes. Ourresults show good extrapolation to new domain geometries and parameters forlong-term temporal simulations. Simulations obtained with MultiScaleGNN arebetween two and four orders of magnitude faster than those on which it wastrained.

Conference paper

Lino M, Fotiadis S, Bharath AA, Cantwell Cet al., 2022, REMuS-GNN: A rotation-equivariant model for simulating continuum dynamics, ICLR 2022 workshop on ‘Geometrical and Topological Representation Learning’, Publisher:

Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice, or when used to explore large parameter spaces. On the other hand, surrogate deep learning models, while accelerating simulations, often exhibit poor accuracy and ability to generalise. In order to improve these two factors, we introduce REMuS-GNN, a rotation-equivariant multi-scale model for simulating continuum dynamical systems encompassing a range of length scales. REMuS-GNN is designed to predict an output vector field from an input vector field on a physical domain discredited into an unstructured set of nodes. Equivariance to rotations of the domain is a desirable inductive bias that allows the network to learn the underlying physics more efficiently, leading to improved accuracy and generalisation compared with similar architectures that lack such symmetry. We demonstrate and evaluate this method on the incompressible flow around elliptical cylinders.

Conference paper

Hossain MZ, Cantwell CD, Sherwin SJ, 2021, A spectral/hp element method for thermal convection, International Journal for Numerical Methods in Fluids, Vol: 93, Pages: 2380-2395, ISSN: 0271-2091

We report on a high‐fidelity, spectral/hp element algorithm developed for the direct numerical simulation of thermal convection problems. We consider the incompressible Navier–Stokes (NS) and advection–diffusion equations coupled through a thermal body‐forcing term. The flow is driven by a prescribed flowrate forcing with explicit treatment of the nonlinear advection terms. The explicit treatment of the body‐force term also decouples both the NS and the advection–diffusion equations. The problem is then temporally discretized using an implicit–explicit scheme in conjunction with a velocity‐correction splitting scheme to decouple the velocity and pressure fields in the momentum equation. Although not unique, this type of discretization has not been widely applied to thermal convection problems and we therefore provide a comprehensive overview of the algorithm and implementation which is available through the open‐source package Nektar++. After verifying the algorithm on a number of illustrative problems we then apply the code to investigate flow in a channel with uniform or streamwise sinusoidal lower wall, in addition to a patterned sinusoidal heating. We verify the solver against previously published two‐dimensional results. Finally, for the first time we consider a three‐dimensional problem with a streamwise sinusoidal lower wall and sinusoidal heating which, for the chosen parameter, leads to the unusual dynamics of an initially unsteady two‐dimensional instability leading to a steady three‐dimensional nonlinear saturated state.

Journal article

Benacchio T, Bonaventura L, Altenbernd M, Cantwell CD, Duben PD, Gillard M, Giraud L, Goeddeke D, Raffin E, Teranishi K, Wedi Net al., 2021, Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction, International Journal of High Performance Computing Applications, Vol: 35, Pages: 285-311, ISSN: 1094-3420

Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

Journal article

Lino M, Cantwell C, Fotiadis S, Pignatelli E, Bharath Aet al., 2020, Simulating surface wave dynamics with convolutional networks, Publisher: arXiv

We investigate the performance of fully convolutional networks to simulatethe motion and interaction of surface waves in open and closed complexgeometries. We focus on a U-Net architecture and analyse how well itgeneralises to geometric configurations not seen during training. Wedemonstrate that a modified U-Net architecture is capable of accuratelypredicting the height distribution of waves on a liquid surface within curvedand multi-faceted open and closed geometries, when only simple box andright-angled corner geometries were seen during training. We also consider aseparate and independent 3D CNN for performing time-interpolation on thepredictions produced by our U-Net. This allows generating simulations with asmaller time-step size than the one the U-Net has been trained for.

Working paper

Ali R, Qureshi N, Liverani S, Roney C, Kim S, Lim P, Tweedy J, Cantwell C, Peters Net al., 2020, Left atrial enhancement correlates with myocardial conduction velocity in patients with persistent atrial fibrillation, Frontiers in Physiology, Vol: 11, ISSN: 1664-042X

Background: Conduction velocity (CV) heterogeneity and myocardial fibrosis both promote re-entry, but the relationship between fibrosis as determined by left atrial (LA) late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMRI) and CV remains uncertain.Objective: Although average CV has been shown to correlate with regional LGE-CMRI in patients with persistent AF, we test the hypothesis that a localized relationship exists to underpin LGE-CMRI as a minimally invasive tool to map myocardial conduction properties for risk stratification and treatment guidance.Method: 3D LA electroanatomic maps during LA pacing were acquired from eight patients with persistent AF following electrical cardioversion. Local CVs were computed using triads of concurrently acquired electrograms and were co-registered to allow correlation with LA wall intensities obtained from LGE-CMRI, quantified using normalized intensity (NI) and image intensity ratio (IIR). Association was evaluated using multilevel linear regression.Results: An association between CV and LGE-CMRI intensity was observed at scales comparable to the size of a mapping electrode: −0.11 m/s per unit increase in NI (P < 0.001) and −0.96 m/s per unit increase in IIR (P < 0.001). The magnitude of this change decreased with larger measurement area. Reproducibility of the association was observed with NI, but not with IIR.Conclusion: At clinically relevant spatial scales, comparable to area of a mapping catheter electrode, LGE-CMRI correlates with CV. Measurement scale is important in accurately quantifying the association of CV and LGE-CMRI intensity. Importantly, NI, but not IIR, accounts for changes in the dynamic range of CMRI and enables quantitative reproducibility of the association.

Journal article

Kim M-Y, Sandler B, Sikkel MB, Cantwell CD, Leong KM, Luther V, Malcolme-Lawes L, Koa-Wing M, Ng FS, Qureshi N, Sohaib A, Whinnett ZI, Fudge M, Lim E, Todd M, Wright I, Peters NS, Lim PB, Linton NWF, Kanagaratnam Pet al., 2020, The ectopy-triggering ganglionated plexuses in atrial fibrillation, Autonomic Neuroscience, Vol: 228, ISSN: 1566-0702

BackgroundEpicardial ganglionated plexus (GP) have an important role in the pathogenesis of atrial fibrillation (AF). The relationship between anatomical, histological and functional effects of GP is not well known. We previously described atrioventricular (AV) dissociating GP (AVD-GP) locations. In this study, we hypothesised that “ET-GP” are upstream triggers of atrial ectopy/AF and have different anatomical distribution to AVD-GP.ObjectivesWe mapped and characterised ET-GP to understand their neural mechanism in AF and anatomical distribution in the left atrium (LA).Methods26 patients with paroxysmal AF were recruited. All were paced in the LA with an ablation catheter. HFS (80 ms) was synchronised to each paced stimulus (after 20 ms delay) for delivery within the local atrial refractory period. HFS responses were tagged onto CARTO™ 3D LA geometry. All geometries were transformed onto one reference LA shell. A probability distribution atlas of ET-GP was created. This identified high/low ET-GP probability regions.Results2302 sites were tested with HFS, identifying 579 (25%) ET-GP. 464 ET-GP were characterised, where 74 (16%) triggered ≥30s AF/AT. Median 97 (IQR 55) sites were tested, identifying 19 (20%) ET-GP per patient. >30% of ET-GP were in the roof, mid-anterior wall, around all PV ostia except in the right inferior PV (RIPV) in the posterior wall.ConclusionET-GP can be identified by endocardial stimulation and their anatomical distribution, in contrast to AVD-GP, would be more likely to be affected by wide antral circumferential ablation. This may contribute to AF ablation outcomes.

Journal article

Brook J, Kim M-Y, Koutsoftidis S, Pitcher D, Agha-Jaffar D, Sufi A, Jenkins C, Tzortzis K, Ma S, Jabbour R, Houston C, Handa B, Li X, Chow J-J, Jothidasan A, Bristow P, Perkins J, Harding S, Bharath A, Ng FS, Peters N, Cantwell C, Chowdhury Ret al., 2020, Development of a pro-arrhythmic ex vivo intact human and porcine model: cardiac electrophysiological changes associated with cellular uncoupling, Pflügers Archiv European Journal of Physiology, Vol: 472, Pages: 1435-1446, ISSN: 0031-6768

We describe a human and large animal Langendorff experimental apparatus for live electrophysiological studies and measure the electrophysiological changes due to gap-junction uncoupling in human and porcine hearts. The resultant ex vivo intact human and porcine model can bridge the translational gap between smaller simple laboratory models and clinical research. In particular, electrophysiological models would benefit from the greater myocardial mass of a large heart due to its effects on far field signal, electrode contact issues and motion artefacts, consequently more closely mimicking the clinical setting Porcine (n=9) and human (n=4) donor hearts were perfused on a custom-designed Langendorff apparatus. Epicardial electrograms were collected at 16 sites across the left atrium and left ventricle. 1mM of carbenoxolone was administered at 5ml/min to induce cellular uncoupling, and then recordings were repeated at the same sites. Changes in electrogram characteristics were analysed.We demonstrate the viability of a controlled ex vivo model of intact porcine and human hearts for electrophysiology with pharmacological modulation. Carbenoxolone reduces cellular coupling and changes contact electrogram features. The time from stimulus artefact to (-dV/dt)max increased between baseline and carbenoxolone (47.9±4.1ms to 67.2±2.7ms) indicating conduction slowing. The features with the largest percentage change between baseline to Carbenoxolone were Fractionation +185.3%, Endpoint amplitude -106.9%, S-Endpoint Gradient +54.9%, S Point, -39.4%, RS Ratio +38.6% and (-dV/dt)max -20.9%.The physiological relevance of this methodological tool is that it provides a model to further investigate pharmacologically-induced proarrhythmic substrates.

Journal article

Kim M-Y, Sandler B, Sikkel MB, Cantwell CD, Leong KM, Luther V, Malcolme-Lawes L, Koa-Wing M, Ng FS, Qureshi N, Sohaib A, Whinnett ZI, Fudge M, Lim E, Todd M, Wright I, Peters NS, Lim PB, Linton NWF, Kanagaratnam Pet al., 2020, The anatomical distribution of the ectopy-triggering ganglionated plexus in patients with atrial fibrillation, Circulation: Arrhythmia and Electrophysiology, Vol: 13, Pages: 1045-1047, ISSN: 1941-3084

Journal article

Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, Dos Santos RW, Wang Let al., 2020, Creation and application of virtual patient cohorts of heart models., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-20, ISSN: 1364-503X

Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Journal article

Clayton RH, Aboelkassem Y, Cantwell CD, Corrado C, Delhaas T, Huberts W, Lei CL, Ni H, Panfilov AV, Roney C, Dos Santos RWet al., 2020, An audit of uncertainty in multi-scale cardiac electrophysiology models., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-21, ISSN: 1364-503X

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Journal article

Lei CL, Ghosh S, Whittaker DG, Aboelkassem Y, Beattie KA, Cantwell CD, Delhaas T, Houston C, Novaes GM, Panfilov AV, Pathmanathan P, Riabiz M, Dos Santos RW, Walmsley J, Worden K, Mirams GR, Wilkinson RDet al., 2020, Considering discrepancy when calibrating a mechanistic electrophysiology model., Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-23, ISSN: 1364-503X

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Journal article

Houston C, Marchand B, Engelbert L, Cantwell CDet al., 2020, Reducing complexity and unidentifiability when modelling human atrial cells, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 378, Pages: 1-17, ISSN: 1364-503X

Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision-making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty and indication of potential issues with selecting a single unique value given the available experimental data. Two approaches are investigated to reduce the uncertainty present: re-calibrating the models to a more complete dataset and using a less complex formulation with fewer parameters to constrain. The re-calibrated models are inserted back into the full cell model to study the overall effect on the action potential. The use of more complete datasets does not eliminate uncertainty present in parameter estimates. The less complex model, particularly for the fast sodium current, gave a better fit to experimental data alongside lower parameter uncertainty and improved computational speed.

Journal article

Fotiadis S, Pignatelli E, Valencia ML, Cantwell C, Storkey A, Bharath AAet al., 2020, Comparing recurrent and convolutional neural networks for predicting wave propagation, Publisher: arXiv

Dynamical systems can be modelled by partial differential equations andnumerical computations are used everywhere in science and engineering. In thiswork, we investigate the performance of recurrent and convolutional deep neuralnetwork architectures to predict the surface waves. The system is governed bythe Saint-Venant equations. We improve on the long-term prediction overprevious methods while keeping the inference time at a fraction of numericalsimulations. We also show that convolutional networks perform at least as wellas recurrent networks in this task. Finally, we assess the generalisationcapability of each network by extrapolating in longer time-frames and indifferent physical settings.

Working paper

Moxey D, Cantwell CD, Bao Y, Cassinelli A, Castiglioni G, Chun S, Juda E, Kazemi E, Lackhove K, Marcon J, Mengaldo G, Serson D, Turner M, Xu H, Peiro J, Kirby RM, Sherwin SJet al., 2020, Nektar++: enhancing the capability and application of high-fidelity spectral/hp element methods, Computer Physics Communications, Vol: 249, Pages: 1-18, ISSN: 0010-4655

Nektar++ is an open-source framework that provides a flexible, high-performance and scalable platform for the development of solvers for partial differential equations using the high-order spectral/ element method. In particular, Nektar++ aims to overcome the complex implementation challenges that are often associated with high-order methods, thereby allowing them to be more readily used in a wide range of application areas. In this paper, we present the algorithmic, implementation and application developments associated with our Nektar++ version 5.0 release. We describe some of the key software and performance developments, including our strategies on parallel I/O, on in situ processing, the use of collective operations for exploiting current and emerging hardware, and interfaces to enable multi-solver coupling. Furthermore, we provide details on a newly developed Python interface that enables a more rapid introduction for new users unfamiliar with spectral/ element methods, C++ and/or Nektar++. This release also incorporates a number of numerical method developments – in particular: the method of moving frames (MMF), which provides an additional approach for the simulation of equations on embedded curvilinear manifolds and domains; a means of handling spatially variable polynomial order; and a novel technique for quasi-3D simulations (which combine a 2D spectral element and 1D Fourier spectral method) to permit spatially-varying perturbations to the geometry in the homogeneous direction. Finally, we demonstrate the new application-level features provided in this release, namely: a facility for generating high-order curvilinear meshes called NekMesh; a novel new AcousticSolver for aeroacoustic problems; our development of a ‘thick’ strip model for the modelling of fluid–structure interaction (FSI) problems in the context of vortex-induced vibrations (VIV). We conclude by commenting on some lessons learned and by discussing some directions fo

Journal article

Sorteberg WE, Garasto S, Cantwell CC, Bharath AAet al., 2020, Approximating the Solution of Surface Wave Propagation Using Deep Neural Networks


Cohen J, Nowell J, Mortari F, Moxey D, Cantwell Cet al., 2019, london-escience/tempss: v0.5

london-escience/tempss: v0.5


Vymazal M, Moxey D, Cantwell CD, Sherwin SJ, Kirby RMet al., 2019, On weak Dirichlet boundary conditions for elliptic problems in the continuous Galerkin method, Journal of Computational Physics, Vol: 394, Pages: 732-744, ISSN: 0021-9991

We combine continuous and discontinuous Galerkin methods in the setting of a model diffusion problem. Starting from a hybrid discontinuous formulation, we replace element interiors by more general subsets of the computational domain – groups of elements that support a piecewise-polynomial continuous expansion. This step allows us to identify a new weak formulation of Dirichlet boundary condition in the continuous framework. We show that the boundary condition leads to a stable discretization with a single parameter insensitive to mesh size and polynomial order of the expansion. The robustness of the approach is demonstrated on several numerical examples.

Journal article

Qureshi N, Kim S, Cantwell C, Afonso V, Bai WJ, Ali R, Shun-Shin M, Louisa M-L, Luther V, Leong K, Lim E, Wright I, Nagy S, Hayat S, Ng FS, Koa-Wing M, Linton N, Lefroy D, Whinnett Z, Davies DW, Kanagaratnam P, Peters N, Lim PBet al., 2019, Voltage during atrial fibrillation is superior to voltage during sinus rhythm in localizing areas of delayed enhancement on magnetic resonance imaging: An assessment of the posterior left atrium in patients with persistent atrial fibrillation, Heart Rhythm, Vol: 16, Pages: 1357-1367, ISSN: 1547-5271

BackgroundBipolar electrogram voltage during sinus rhythm (VSR) has been used as a surrogate for atrial fibrosis in guiding catheter ablation of persistent AF, but the fixed rate and wavefront characteristics present during sinus rhythm may not accurately reflect underlying functional vulnerabilities responsible for AF maintenance.ObjectivesWe hypothesized that given adequate temporal sampling, the spatial distribution of mean AF voltage (VmAF) should better correlate with delayed-enhancement MRI (MRI-DE) detected atrial fibrosis than VSR.MethodsAF was mapped (8s) during index ablation for persistent AF (20 patients) using a 20-pole catheter (660±28 points/map). Following cardioversion, VSR was mapped (557±326 points/map). Electroanatomic and MRI-DE maps were co-registered in 14 patients.Results(i) The time course of VmAF was assessed from 1-40 AF-cycles (∼8s) at 1113 locations. VmAF stabilized with sampling >4s (mean voltage error=0.05mV). (ii) Paired point analysis of VmAF from segments acquired 30s apart (3,667-sites, 15-patients), showed strong correlation (r=0.95, p<0.001). (iii) Delayed-enhancement (DE) was assessed across the posterior left atrial (LA) wall, occupying 33±13%. VmAF distributions (median[IQR]) were 0.21[0.14-0.35]mV in DE vs. 0.52[0.34-0.77]mV in Non-DE regions. VSR distributions were 1.34[0.65-2.48]mV in DE vs. 2.37[1.27-3.97]mV in Non-DE. A VmAF threshold of 0.35mV yielded sensitivity/specificity 75%/79% in detecting MRI-DE, compared with 63%/67% for VSR (1.8mV threshold).ConclusionThe correlation between low-voltage and posterior LA MRI-DE is significantly improved when acquired during AF vs. sinus rhythm. With adequate sampling, mean AF voltage is a reproducible marker reflecting the functional response to the underlying persistent AF substrate.

Journal article

Ng FS, Roney C, Cantwell CD, Peters NSet al., 2019, Fundamentals of cardiac mapping, Cardiac Mapping, Pages: 70-83, ISBN: 9781119152590

This chapter describes the different approaches to mapping arrhythmias in the electrophysiology laboratory, including conventional contact catheter mapping techniques and maneuvers, three-dimensional electroanatomical mapping using both contact and non-contact recordings, and also newer mapping approaches for myocardial fibrillation such as phase mapping and dominant frequency mapping. A unipolar electrogram is recorded between an electrode in contact with the myocardium and a fixed distant reference point. In contrast, a bipolar electrogram is recorded between two closely spaced electrodes on the recording catheter, and is derived as the difference between the unipolar electrograms recorded from the two electrodes. Several classic methods exist for mapping arrhythmia foci using contact catheters. A commonly used method is activation sequence mapping. Pace mapping for focal tachycardias is often used in combination with activation mapping. Entrainment can help in distinguishing reentrant tachycardias from those resulting from automaticity or triggered activity.

Book chapter

Cantwell C, Mohamied Y, Tzortzis K, Garasto S, Houston C, Chowdhury R, Ng F, Bharath A, Peters Net al., 2019, Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling, Computers in Biology and Medicine, Vol: 104, Pages: 339-351, ISSN: 0010-4825

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localised destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.

Journal article

Sorteberg W, Garasto S, Cantwell C, Bharath Aet al., 2019, Approximating the solution of Surface Wave Propagation Using Deep Neural Networks, INNS Big Data and Deep Learning 2019, Publisher: Springer, ISSN: 2661-8141

Partial differential equations formalise the understanding of the behaviour of the physical world that humans acquire through experience and observation. Through their numerical solution, such equations are used to model and predict the evolution of dynamical systems. However, such techniques require extensive computational resources and assume the physics are prescribed \textit{a priori}. Here, we propose a neural network capable of predicting the evolution of a specific physical phenomenon: propagation of surface waves enclosed in a tank, which, mathematically, can be described by the Saint-Venant equations. The existence of reflections and interference makes this problem non-trivial. Forecasting of future states (i.e. spatial patterns of rendered wave amplitude) is achieved from a relatively small set of initial observations. Using a network to make approximate but rapid predictions would enable the active, real-time control of physical systems, often required for engineering design. We used a deep neural network comprising of three main blocks: an encoder, a propagator with three parallel Long Short-Term Memory layers, and a decoder. Results on a novel, custom dataset of simulated sequences produced by a numerical solver show reasonable predictions for as long as 80 time steps into the future on a hold-out dataset. Furthermore, we show that the network is capable of generalising to two other initial conditions that are qualitatively different from those seen at training time.

Conference paper

Cantwell C, Nielsen A, 2019, A minimally intrusive low-memory approach to resilience for existing transient solvers, Journal of Scientific Computing, Vol: 78, Pages: 565-581, ISSN: 0885-7474

We propose a novel, minimally intrusive approach to adding fault tolerance to existing complex scientific simulation codes, used for addressing a broad range of time-dependent problems on the next generation of supercomputers. Exascale systems have the potential to allow much larger, more accurate and scale-resolving simulations of transient processes than can be performed on current petascale systems. However, with a much larger number of components, exascale computers are expected to suffer a node failure every few minutes. Many existing parallel simulation codes are not tolerant of these failures and existing resilience methodologies would necessitate major modifications or redesign of the application. Our approach combines the proposed user-level failure mitigation extensions to the Message-Passing Interface (MPI), with the concepts of message-logging and remote in-memory checkpointing, to demonstrate how to add scalable resilience to transient solvers. Logging MPI communication reduces the storage requirement of static data, such as finite element operators, and allows a spare MPI process to rebuild these data structures independently of other ranks. Remote in-memory checkpointing avoids disk I/O contention on large parallel filesystems. A prototype implementation is applied to Nektar++, a scalable, production-ready transient simulation framework. Forward-path and recovery-path performance of the resilience algorithm is analysed through experiments using the solver for the incompressible Navier-Stokes equations, and strong scaling of the approach is observed.

Journal article

Kim M-Y, Sikkel MB, Hunter RJ, Haywood GA, Tomlinson DR, Tayebjee MH, Ali R, Cantwell CD, Gonna H, Sandler B, Limb E, Furniss G, Mrcp DP, Begg G, Dhillon G, Hill NJ, O'Neill J, Francis DP, Lim PB, Peters NS, Linton NWF, Kanagaratnam Pet al., 2018, A novel approach to mapping the atrial ganglionated plexus network by generating a distribution probability atlas, Journal of Cardiovascular Electrophysiology, Vol: 29, Pages: 1624-1634, ISSN: 1045-3873

INTRODUCTION: The ganglionated plexuses (GPs) of the intrinsic cardiac autonomic system are implicated in arrhythmogenesis. GP localization by stimulation of the epicardial fat pads to produce atrioventricular dissociating (AVD) effects is well described. We determined the anatomical distribution of the left atrial GPs that influence AV dissociation. METHODS AND RESULTS: High frequency stimulation was delivered through a Smart-Touch™ catheter in the left atrium of patients undergoing atrial fibrillation (AF) ablation. 3D locations of points tested throughout the entire chamber were recorded on the CARTO™ system. Impact on the AV conduction was categorized as ventricular asystole, bradycardia or no effect. CARTO™ maps were exported, registered and transformed onto a reference left atrial geometry using a custom software, enabling data from multiple patients to be overlaid. In 28 patients, 2108 locations were tested and 283 sites (13%) demonstrated atrioventricular dissociation effects (AVD-GP). There were 10 AVD-GPs (IQR 11.5) per patient. 80% (226) produced asystole and 20% (57) showed bradycardia. The distribution of the two groups were very similar. Highest probability of AVD-GPs (>20%) were identified in: infero-septal portion (41%) and right inferior pulmonary vein base (30%) of the posterior wall, right superior pulmonary vein antrum (31%). CONCLUSION: It is feasible to map the entire left atrium for AVD-GPs prior to AF ablation. Aggregated data from multiple patients, producing a distribution probability atlas of AVD-GPs, identified three regions with a higher likelihood for finding AVD-GPs and these matched the histological descriptions. This approach could be used to better characterise the autonomic network. This article is protected by copyright. All rights reserved.

Journal article

Handa BS, Roney CH, Houston C, Qureshi N, Li X, Pitcher DS, Chowdhury RA, Lim PB, Dupont E, Niederer S, Cantwell C, Peters NS, Ng FSet al., 2018, Analytical approaches for myocardial fibrillation signals, Computers in Biology and Medicine, Vol: 102, Pages: 315-326, ISSN: 0010-4825

Atrial and ventricular fibrillation are complex arrhythmias, and their underlying mechanisms remain widely debated and incompletely understood. This is partly because the electrical signals recorded during myocardial fibrillation are themselves complex and difficult to interpret with simple analytical tools. There are currently a number of analytical approaches to handle fibrillation data. Some of these techniques focus on mapping putative drivers of myocardial fibrillation, such as dominant frequency, organizational index, Shannon entropy and phase mapping. Other techniques focus on mapping the underlying myocardial substrate sustaining fibrillation, such as voltage mapping and complex fractionated electrogram mapping. In this review, we discuss these techniques, their application and their limitations, with reference to our experimental and clinical data. We also describe novel tools including a new algorithm to map microreentrant circuits sustaining fibrillation.

Journal article

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