Fetching the paper…
Reading the bibliography…
Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure.
Multilayer feedforward networks are universal approximators,
K. Hornik, M. Stinchcombe, H. White, · 1989
Earlier work this paper cites.
A hybrid neural network-first principles approach to process modeling,
D. C. Psichogios, L. H. Ungar, · 1992
Earlier work this paper cites.
Continuous-time nonlinear signal processing: a neural network based approach for gray box identification,
R. Rico-Martinez, J. Anderson, I. Kevrekidis, · 1994
Earlier work this paper cites.
A direct numerical simulation study of flow past a freely vibrating cable,
D. J. Newman, G. E. Karniadakis, · 1997
Earlier work this paper cites.
M. P. Paidoussis, Fluid-Structure Interactions: Slender Structures and Axial Flow volume 1, Academic Press, 1998
1998
Earlier work this paper cites.
Artificial neural networks for solving ordinary and partial differential equations,
I. E. Lagaris, A. Likas, D. I. Fotiadis, · 1998
Earlier work this paper cites.
Dynamics and flow structures in the turbulent wake of rigid and flexible cylinders subject to vortex-induced vibrations,
C. Evangelinos, G. E. Karniadakis, · 1999
Earlier work this paper cites.
Neural network modeling for near wall turbulent flow,
M. Milano, P. Koumoutsakos, · 2002
Earlier work this paper cites.
M. P. Paidoussis, Fluid-Structure Interactions: Slender Structures and Axial Flow volume 2, Academic Press, 2004
2004
Earlier work this paper cites.
Vortex-induced vibration,
C. H. K. Williamson, R. Govardhan, · 2004
Earlier work this paper cites.
G. E. Karniadakis, S. Sherwin, Spectral/hp Element Methods for Computational Fluid Dynamics, 2nd edition, Oxford University Press, Oxford,UK, 2005
2005
Earlier work this paper cites.
C. E. Rasmussen, C. K. Williams, Gaussian processes for machine learning, volume 1, MIT press Cambridge, 2006
2006
Earlier work this paper cites.
Solving initial-boundary value problems for systems of partial differential equations using neural networks and optimization techniques,
R. S. Beidokhti, A. Malek, · 2009
Earlier work this paper cites.
Vortex-induced vibrations of a long flexible cylinder in shear flow,
R. Bourguet, G. E. Karniadakis, M. S. Triantafyllou, · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization,
D. P. Kingma, J. Ba, · 2014
Earlier work this paper cites.
Bayesian numerical homogenization,
H. Owhadi, · 2015
Earlier work this paper cites.
Brittleness of Bayesian inference under finite information in a continuous world,
H. Owhadi, C. Scovel, T. Sullivan, et al., · 2015
Cited alongside, same era.
Automatic differentiation in machine learning: a survey,
A. G. Baydin, B. A. Pearlmutter, A. A. Radul, J. M. Siskind, · 2015
Cited alongside, same era.
New approaches in turbulence and transition modeling using data-driven techniques,
K. Duraisamy, Z. J. Zhang, A. P. Singh, · 2015
Cited alongside, same era.
Machine learning methods for data-driven turbulence modeling,
Z. J. Zhang, K. Duraisamy, · 2015
Cited alongside, same era.
Evaluation of machine learning algorithms for prediction of regions of high reynolds averaged navier stokes uncertainty,
J. Ling, J. Templeton, · 2015
Cited alongside, same era.
Parametric Gaussian process regression for big data,
M. Raissi, · 2017
Later among the works it cites.
Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling,
P. Perdikaris, M. Raissi, A. Damianou, N. D. Lawrence, G. E. Karniadakis, · 2017
Later among the works it cites.
Deep hidden physics models: Deep learning of nonlinear partial differential equations,
M. Raissi, · 2018
Closest in time.
Numerical gaussian processes for time-dependent and nonlinear partial differential equations,
M. Raissi, P. Perdikaris, G. E. Karniadakis, · 2018
Closest in time.
Hidden physics models: Machine learning of nonlinear partial differential equations,
M. Raissi, G. E. Karniadakis, · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A paradigm for data-driven predictive modeling using field inversion and machine learning,
E. J. Parish, K. Duraisamy, · 2016
Cited alongside, same era.
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance,
J. Ling, A. Kurzawski, J. Templeton, · 2016
Cited alongside, same era.
Multifidelity information fusion algorithms for high-dimensional systems and massive data sets,
P. Perdikaris, D. Venturi, G. E. Karniadakis, · 2016
Cited alongside, same era.
Understanding deep convolutional networks,
S. Mallat, · 2016
Cited alongside, same era.
Deep multi-fidelity Gaussian processes,
M. Raissi, G. Karniadakis, · 2016
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems,
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al., · 2016
Cited alongside, same era.
On the expressive power of deep neural networks,
M. Raghu, B. Poole, J. Kleinberg, S. Ganguli, J. Sohl-Dickstein, · 2016
Cited alongside, same era.
G. Pang, L. Yang, G. E. Karniadakis, · 2018
Closest in time.
Y. Zhu, N. Zabaras, · 2018
Closest in time.
R. Tripathy, I. Bilionis, · 2018
Closest in time.
Data-driven forecasting of high-dimensional chaotic systems with long-short term memory networks,
P. R. Vlachas, W. Byeon, Z. Y. Wan, T. P. Sapsis, P. Koumoutsakos, · 2018
Closest in time.
R. Kondor, · 2018
Closest in time.
R. Kondor, S. Trivedi, · 2018
Closest in time.
M. Raissi, · 2018
Closest in time.
Multistep neural networks for data-driven discovery of nonlinear dynamical systems,
M. Raissi, P. Perdikaris, G. E. Karniadakis, · 2018
Closest in time.
M. Raissi, A. Yazdani, G. E. Karniadakis, · 2018
Closest in time.
Neural ordinary differential equations,
T. Q. Chen, Y. Rubanova, J. Bettencourt, D. Duvenaud, · 2018
Closest in time.