References:

References
[1] International Civil Aviation Organisation. “The World of Air Transport in 2019”. In:
(2019). url: https://www.icao.int/annual-report-2019/Pages/the-world-of-airtransport-
in-2019.aspx.
[2] International Air Transport Association. “IATA/Tourism Economics Air Passenger Forecast,
March 2022”. In: (2022). url: https://www.iata.org/en/pressroom/2022-releases/
2022-03-01-01/.
[3] Corinne Le Quéré et al. Supplementary data to: Le Quéré et al (2020), Temporary reduction
in daily global CO2 emissions during the COVID-19 forced confinement. 2020. url: https:
//www.icos-cp.eu/gcp-covid19.
[4] John A. Ekaterinaris. “High-order accurate, low numerical diffusion methods for aerodynamics”.
In: Progress in Aerospace Sciences 41.3 (2005), pp. 192–300. issn: 0376-0421. url: https:
//www.sciencedirect.com/science/article/pii/S0376042105000473.
[5] Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd. USA:
Prentice Hall Press, 2009. isbn: 0136042597.
[6] Deep Blue computer beats world chess champion. 1996. url: https://www.theguardian.
com/sport/2021/feb/12/deep-blue-computer-beats-kasparov-chess-1996.
[7] Sam Adams et al. “Mapping the Landscape of Human-Level Artificial General Intelligence”.
In: AI Magazine 33 (Mar. 2012), pp. 25–42.
[8] Hubert L. Dreyfus. What Computers Still Can’t Do: A Critique of Artificial Reason.
Cambridge, MA, USA: MIT Press, 1992. isbn: 0262041340.
[9] Ragnar Fjelland. “Why general artificial intelligence will not be realized”. In: Humanities
and Social Sciences Communications 7 (June 2020), p. 10.
61
[10] Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. http : / / www .
deeplearningbook.org. MIT Press, 2016.
[11] Warren Mcculloch and Walter Pitts. “A Logical Calculus of Ideas Immanent in Nervous
Activity”. In: Bulletin of Mathematical Biophysics 5 (1943), pp. 127–147.
[12] Geoffrey E. Hinton, Simon Osindero and Yee-Whye Teh. “A Fast Learning Algorithm for
Deep Belief Nets”. In: Neural Computation 18.7 (2006), pp. 1527–1554. issn: 0899-7667.
eprint: https://direct.mit.edu/neco/article-pdf/18/7/1527/816558/neco.2006.
18.7.1527.pdf.
[13] Y. Taigman et al. “DeepFace: Closing the Gap to Human-Level Performance in Face
Verification”. In: (2014), pp. 1701–1708.
[14] D. Stuart Pope Thomas F. Brooks and Michael A. Marcolini. Airfoil Self-Noise Data Set.
2014. url: https://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise#.
[15] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. Second.
The MIT Press, 2018. url: http://incompleteideas.net/book/the-book-2nd.html.
[16] Volodymyr Mnih et al. “Playing Atari with Deep Reinforcement Learning”. In: CoRR
abs/1312.5602 (2013). arXiv: 1312.5602. url: http://arxiv.org/abs/1312.5602.
[17] Xindong Wu et al. “Top 10 algorithms in data mining”. In: Knowledge and Information
Systems 14 (2007).
[18] Daniel P. Raymer. Aircraft Design: A Conceptual Approach. 5th. AIAA Education Series.
[19] Nicolas Bons and Joaquim Martins. “Aerostructural Wing Design Exploration with
Multidisciplinary Design Optimization”. In: (2020).
[20] Aerospace and AI - Bringing together Montreal’s distinctive strengths. 2019. url: https:
/ / www . bcg . com / en - ca / aerospace - and - ai - bringing - together - montreals -
distinctive-strengths.
[21] IVADO and Bombardier Sign a Strategic Agreement to Develop Innovative Digital Solutions.
2019. url: https://ivado.ca/en/2019/02/20/ivado- and- bombardier- sign- astrategic-
agreement-to-develop-innovative-digital-solutions/.
[22] SCALE AI. “Predictive Analytics for Aviation”. In: (2021). url: https://www.scaleai.
ca/funded-projects/predictive-analytics-for-aviation/.
62
[23] Emmanuel J. Candès et al. “Robust Principal Component Analysis?” In: J. ACM 58.3
(2011). issn: 0004-5411.
[24] Krithika Manohar et al. “Predicting shim gaps in aircraft assembly with machine learning
and sparse sensing”. In: Journal of Manufacturing Systems 48 (2018), pp. 87–95. issn: 0278-
6125. url: https://www.sciencedirect.com/science/article/pii/S0278612518300116.
[25] K. Manohar et al. “Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating
the Benefits of Exploiting Known Patterns”. In: IEEE Control Systems Magazine 38.3
(2018), pp. 63–86.
[26] D. Bruneo and F. De Vita. “On the Use of LSTM Networks for Predictive Maintenance in
Smart Industries”. In: (2019), pp. 241–248.
[27] A. Nanduri and L. Sherry. “Anomaly detection in aircraft data using Recurrent Neural
Networks (RNN)”. In: (2016), pp. 5C2–1–5C2–8.
[28] Liang Xu et al. “Guided Wave-Convolutional Neural Network Based Fatigue Crack
Diagnosis of Aircraft Structures”. In: Sensors 19.16 (2019). issn: 1424-8220.
[29] Paul Garnier et al. A review on Deep Reinforcement Learning for Fluid Mechanics. 2021.
arXiv: 1908.04127 [physics.comp-ph].
[30] J. Rabault et al. “Deep reinforcement learning in fluid mechanics: A promising method for
both active flow control and shape optimization”. In: Journal of Hydrodynamics 32 (2020),
pp. 234–246.
[31] Tony Hey, Stewart Tansley and Kristin Tolle. The Fourth Paradigm: Data-Intensive
Scientific Discovery. Microsoft Research, 2009. isbn: 978-0-9825442-0-4. url: https :
/ / www . microsoft . com / en - us / research / publication / fourth - paradigm - data -
intensive-scientific-discovery/.
[32] B.C. Vermeire, F.D. Witherden and P.E. Vincent. “On the utility of GPU accelerated
high-order methods for unsteady flow simulations: A comparison with industry-standard
tools”. In: Journal of Computational Physics 334 (2017), pp. 497–521. issn: 0021-9991.
[33] Martín Abadi et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.
Software available from tensorflow.org. 2015. url: https://www.tensorflow.org/.
63
[34] David Donoho. “50 Years of Data Science”. In: Journal of Computational and Graphical
Statistics 26.4 (2017), pp. 745–766. eprint: https://doi.org/10.1080/10618600.2017.
1384734.
[35] Masataka Gamahara and Yuji Hattori. “Searching for turbulence models by artificial
neural network”. In: Physical Review Fluids 2 (5 2017), p. 054604.
[36] Julia Ling, Reese Jones and Jeremy Templeton. “Machine learning strategies for systems
with invariance properties”. In: Journal of Computational Physics 318 (2016).
[37] S. B. Pope. “A more general effective-viscosity hypothesis”. In: Journal of Fluid Mechanics
72.2 (1975), 331–340.
[38] Salar Taghizadeh, Freddie D Witherden and Sharath S Girimaji. “Turbulence closure
modeling with data-driven techniques: physical compatibility and consistency considerations”.
In: New Journal of Physics 22.9 (2020), p. 093023. issn: 1367-2630.
[39] F.D. Witherden, A.M. Farrington and P.E. Vincent. “PyFR: An open source framework
for solving advection–diffusion type problems on streaming architectures using the flux
reconstruction approach”. In: Computer Physics Communications 185.11 (2014), pp. 3028–3040.
issn: 0010-4655. url: https : / / www . sciencedirect . com / science / article / pii /
S0010465514002549.
[40] John Kim, Parviz Moin and Robert Moser. “The Turbulence Statistics in Fully Developed
Channel Flow at Low Reynolds Number”. In: Journal of Fluid Mechanics 177 (May 1987).
[41] Ivette Rodriguez et al. “Direct numerical simulation of a NACA0012 in full stall”. In:
International Journal of Heat and Fluid Flow 43 (2013), pp. 194–203.
[42] P.A. Davidson. Turbulence: An Introduction for Scientists and Engineers. OUP Oxford,
2004. isbn: 9780191589850. url: https://books.google.ca/books?id=e\_tzBAAAQBAJ.
[43] B.C. Vermeire, Carlos Pereira and Hamid R. Karbasian. Computational Fluid Dynamics:
An Open-Source Approach. 2021.
[44] H.T.Huynh. “A Flux Reconstruction Approach to High-order Schemes Including Discontinuous
Galerkin Methods”. In: In 18th AIAA Computational Fluid Dynamics Conference (2007),
p. 4079.
[45] Uriel Frisch. Turbulence: The Legacy of A.N. Kolmogorov. Nov. 1995. isbn: 9780521451031.
64
[46] Osborne Reynolds. “IV. On the dynamical theory of incompressible viscous fluids and
the determination of the criterion”. In: Philosophical Transactions of the Royal Society of
London. (A.) 186 (1895), pp. 123–164.
[47] P. Y. Chou. “On velocity correlations and the solutions of the equations of turbulent
fluctuation”. In: Quarterly of Applied Mathematics 3.1 (1945), pp. 38–54.
[48] M.A. Leschziner and F.-S. Lien. “Numerical Aspects of Applying Second-Moment Closure
to Complex Flows”. In: Closure Strategies for Turbulent and Transitional Flows. Ed. by
B. E. Launder and N. D.Editors Sandham. Cambridge University Press, 2002, 153–187.
[49] C G Speziale. “Analytical Methods for the Development of Reynolds-Stress Closures in
Turbulence”. In: Annual Review of Fluid Mechanics 23.1 (1991), pp. 107–157. eprint:
https://doi.org/10.1146/annurev.fl.23.010191.000543.
[50] Stephen B Pope. Turbulent flows. Cambridge: Cambridge Univ. Press, 2011. url: https:
//cds.cern.ch/record/1346971.
[51] T. B. Gatski and C. G. Speziale. “On explicit algebraic stress models for complex turbulent
flows”. In: Journal of Fluid Mechanics 254 (1993), 59–78.
[52] R. A. Fisher. “The Use of Multiple Measurements in Taxonomic Problems”. In: Annals of
Eugenics 7.7 (1936), pp. 179–188.
[53] Hugo Larochelle. “Études de techniques d’appentissage non-supervisé pour l’amélioration de
l’entraînement supervisé de modèles connexionnistes”. PhD thesis. Université de Montréal,
2009.
[54] B. Liu and Y. Liu. “Image Classification for Dogs and Cats”. In: (2014).
[55] Leslie N. Smith. Cyclical Learning Rates for Training Neural Networks. 2015. url: https:
//arxiv.org/abs/1506.01186.
[56] Dheeru Dua and Casey Graff. UCI Machine Learning Repository. 2017. url: http :
//archive.ics.uci.edu/ml.
[57] Kaiming He et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification. 2015. url: https://arxiv.org/abs/1502.01852.
[58] Abien Fred Agarap. “Deep learning using rectified linear units (relu)”. In: arXiv preprint
arXiv:1803.08375 (2018).
[59] Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. 2014.
65
[60] Nitish Srivastava et al. “Dropout: A Simple Way to Prevent Neural Networks from
Overfitting”. In: J. Mach. Learn. Res. 15.1 (2014), 1929–1958. issn: 1532-4435.
[61] P.A. Davidson. Turbulence: An Introduction for Scientists and Engineers. OUP Oxford,
2004. isbn: 9780191589850. url: https://books.google.ca/books?id=e\_tzBAAAQBAJ.
[62] Rui Wang et al. “Towards Physics-Informed Deep Learning for Turbulent Flow Prediction”.
In: (2020).
[63] Fangying Song and George Em Karniadakis. A Universal Fractional Model of Wall-
Turbulence. 2018.