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Extreme events in society and nature, such as pandemic spikes, rogue waves, or structural failures, can have catastrophic consequences.
A contribution to the mathematical theory of epidemics
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On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
W. R. Thompson · 1933
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K. Karhunen · 1947
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V. E. Zakharov, P. Guyenne, A. N. Pushkarev, and F. Dias · 2001
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Observed platform response to a monster wave
O. E. Hansteen, H. P. Jostad, and T. I. Tjelta · 2003
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C. E. Rasmussen · 2003
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One-dimensional wave turbulence
V. E. Zakharov, F. Dias, and A. Pushkarev · 2004
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National Academies Press
Creating a disaster resilient america: Grand challenges in science and technology · 2005
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A hysteretic cohesive-law model of fatigue-crack nucleation
S. Serebrinsky and M. Ortiz · 2005
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Sparse Gaussian processes using pseudo-inputs
E. Snelson and Z. Ghahramani · 2006
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Dynamic Response and Fatigue Reliability Analysis of Marine Riser Under Random Loads
R. A. Khan and S. Ahmad · 2007
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Lock-in, transient and chaotic response in riser viv
F. Chasparis, Y. Modarres-Sadeghi, F. S. Hover, M. S. Triantafyllou, M. Tognarelli, and P. Beynet · 2009
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Adaptive design and analysis of supercomputer experiments
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Variational learning of inducing variables in sparse Gaussian processes
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Numerical simulations of surface effect ship in waves
W.-M. Lin, S. Zhang, and K. M. Weems · 2010
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An empirical evaluation of Thompson sampling
O. Chapelle and L. Li · 2011
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Ak-mcs: an active learning reliability method combining kriging and monte carlo simulation
B. Echard, N. Gayton, and M. Lemaire · 2011
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Quasibreathers in the MMT model
A. Pushkarev and V. E. Zakharov · 2013
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Quantification and prediction of extreme events in a one-dimensional nonlinear dispersive wave model
W. Cousins and T. P. Sapsis · 2014
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Weight uncertainty in neural network
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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Taking the human out of the loop: A review of Bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas · 2015
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Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples
T. P. Sapsis · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
A. G. Wilson and P. Izmailov · 2020
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An active learning method combining deep neural network and weighted sampling for structural reliability analysis
Z. Xiang, J. Chen, Y. Bao, and H. Li · 2020
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Influence of deck submergence events on extreme properties of wave-induced vertical bending moment
V. Belenky, K. Weems, T. P. Sapsis, and V. Pipiras · 2021
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Output-weighted optimal sampling for bayesian experimental design and uncertainty quantification
A. Blanchard and T. Sapsis · 2021
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L. N. Smith · 2015
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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SGDR: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2016
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Deep bayesian active learning with image data
Y. Gal, R. Islam, and Z. Ghahramani · 2017
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Snapshot ensembles: Train 1, get m for free
G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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Active discriminative text representation learning
Y. Zhang, M. Lease, and B. Wallace · 2017
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Bayesian optimization with output-weighted optimal sampling
A. Blanchard and T. P. Sapsis · 2021
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A survey of uncertainty in deep neural networks
J. Gawlikowski, C. R. N. Tassi, M. Ali, J. Lee, M. Humt, J. Feng, A. Kruspe, R. Triebel, P. Jung, R. Roscher, et al · 2021
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Neural operator: Learning maps between function spaces
N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2021
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
L. Lu, P. Jin, G. Pang, Z. Zhang, and G. E. Karniadakis · 2021
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Deep double descent: Where bigger models and more data hurt
P. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, and I. Sutskever · 2021
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A survey of deep active learning
P. Ren, Y. Xiao, X. Chang, P. Y. Huang, Z. Li, B. B. Gupta, X. Chen, and X. Wang · 2021
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Statistics of extreme events in fluid flows and waves
T. P. Sapsis · 2021
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Extreme properties of impact-induced vertical bending moments
T. P. Sapsis, V. Belenky, K. Weems, and V. Pipiras · 2021
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Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation
F. Uribe, I. Papaioannou, Y. M. Marzouk, and D. Straub · 2021
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Output-weighted sampling for multi-armed bandits with extreme payoffs
Y. Yang, A. Blanchard, T. P. Sapsis, and P. Perdikaris · 2021
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Sequential active learning of low-dimensional model representations for reliability analysis
M. Ehre, I. Papaioannou, B. Sudret, and D. Straub · 2022
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Generation mechanism and prediction of an observed extreme rogue wave
J. Gemmrich and L. Cicon · 2022
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Wavegroup-based gaussian process regression for ship dynamics: Between the Scylla of slow Karhunen-Loève convergence and the Charybdis of transient features
S. Guth and T. P. Sapsis · 2022
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Information FOMO: The unhealthy fear of missing out on information. a method for removing misleading data for healthier models
E. Pickering and T. Sapsis · 2022
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E. Pickering and T. P. Sapsis · 2022
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Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
A. F. Psaros, X. Meng, Z. Zou, L. Guo, and G. E. Karniadakis · 2022
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Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modelling with gaussian process regression
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