IEEE Computational Intelligence Magazine - May 2021 - 30
[8] H. Lee and I. S. Kang, " Neural algorithm for solving differential equations, " Comput.
Phys., vol. 91, no. 1, pp. 110-131, 1990. doi: 10.1016/0021-9991(90)90007-N.
[9] A. Meade and A. Fernandez, " Solution of nonlinear ordinary differential equations by
feedforward neural networks, " Math. Comput. Model., vol. 20, no. 9, pp. 19-44, 1994. doi:
10.1016/0895-7177(94)00160-X.
[10] A. Meade and A. Fernandez, " The numerical solution of linear ordinary differential equations by feedforward neural networks, " Math. Computer Model., vol. 19, no. 12,
pp. 1-25, 1994. doi: 10.1016/0895-7177(94)90095-7.
[11] M. W. M. G. Dissanayake and N. Phan-Thien, " Neural-network-based approximations for solving partial differential equations, " Commun. Numer. Methods Eng., vol. 10,
no. 3, pp. 195-201, 1994. doi: 10.1002/cnm.1640100303.
[12] I. Lagaris, A. Likas, and D. Fotiadis, " Artificial neural network methods in quantum
mechanics, " Comput. Phys. Commun., vol. 104, nos. 1-3, pp. 1-14, 1997. doi: 10.1016/
S0010-4655(97)00054-4.
[13] I. Lagaris, A. Likas, and D. Fotiadis, " Artificial neural networks for solving ordinary
and partial differential equations, " IEEE Trans. Neural Netw., vol. 9, no. 5, pp. 987-1000,
1998. doi: 10.1109/72.712178.
[14] I. Lagaris, A. Likas, and D. Papageorgiou, " Neural-network methods for boundary value problems with irregular boundaries, " IEEE Trans. Neural Netw., vol. 11, no. 5,
pp. 1041-1049, 2000. doi: 10.1109/72.870037.
[15] K. McFall and J. Mahan, " Artificial neural network method for solution of boundary
value problems with exact satisfaction of arbitrary boundary conditions, " IEEE Trans.
Neural Netw., vol. 20, no. 8, pp. 1221-1233, 2009. doi: 10.1109/TNN.2009.2020735.
[16] K. Rudd, G. D. Muro, and S. Ferrari, " A constrained backpropagation approach for
the adaptive solution of partial differential equations, " IEEE Trans. Neural Netw. Learn.
Syst., vol. 25, no. 3, pp. 571-584, 2014. doi: 10.1109/TNNLS.2013.2277601.
[17] K. Rudd and S. Ferrari, " A constrained integration (CINT) approach to solving
partial differential equations using artificial neural networks, " Neurocomputing, vol. 155,
pp. 277-285, May 2015. doi: 10.1016/j.neucom.2014.11.058.
[18] J. Berg and K. Nyström, " A unified deep artificial neural network approach to partial
differential equations in complex geometries, " Neurocomputing, vol. 317, pp. 28-41, Nov.
2018. doi: 10.1016/j.neucom.2018.06.056.
[19] J. Sirignano and K. Spiliopoulos, " DGM: A deep learning algorithm for solving
partial differential equations, " Comput. Phys., vol. 375, pp. 1339-1364, Dec. 2018. doi:
10.1016/j.jcp.2018.08.029.
[20] C. Anitescu, E. Atroshchenko, N. Alajlan, and T. Rabczuk, " Artificial neural network methods for the solution of second order boundary value problems, " Comput. Mater.
Cont., vol. 59, no. 1, pp. 345-359, 2019. doi: 10.32604/cmc.2019.06641.
[21] N. Geneva and N. Zabaras, " Modeling the dynamics of PDE systems with physicsconstrained deep auto-regressive networks, " Comput. Phys., vol. 403, pp 109,056, Feb.
2020. doi: 10.1016/j.jcp.2019.109056.
[22] A. D. Jagtap, K. Kawaguchi, and G. E. Karniadakis, " Adaptive activation functions
accelerate convergence in deep and physics-informed neural networks, " Comput. Phys.,
vol. 404, p. 109,136, Mar. 2020. doi: 10.1016/j.jcp.2019.109136.
[23] Z. Mao, A. D. Jagtap, and G. E. Karniadakis, " Physics-informed neural networks for
high-speed f lows, " Comput. Methods Appl. Mech. Eng., vol. 360, p. 112,789, Mar. 2020.
doi: 10.1016/j.cma.2019.112789.
[24] M. Raissi, P. Perdikaris, and G. Karniadakis, " Physics-informed neural networks:
A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, " Comput. Phys., vol. 378, pp. 686-707, Feb. 2019. doi:
10.1016/j.jcp.2018.10.045.
[25] J. Peiró and S. Sherwin, " Finite difference, finite element and finite volume methods
for partial differential equations, " in Handbook of Materials Modeling. Springer Netherlands,
2005, pp. 2415-2446.
[26] Y. Chen, L. Lu, G. E. Karniadakis, and L. D. Negro, " Physics-informed neural
networks for inverse problems in nano-optics and metamaterials, " Opt. Express, vol. 28,
no. 8, p. 11,618, Apr. 2020. doi: 10.1364/OE.384875.
[27] M. Raissi, " Deep hidden physics models: Deep learning of nonlinear partial differential equations, " J. Mach. Learn. Res., vol. 19, no. 25, pp. 1-24, 2018. [Online]. Available:
http://jmlr.org/papers/v19/18-046.html
[28] Y. Yang and P. Perdikaris, " Adversarial uncertainty quantification in physics-informed neural networks, " Comput. Phys., vol. 394, pp. 136-152, Oct. 2019. doi: 10.1016/
j.jcp.2019.05.027.
[29] M. Raissi, Z. Wang, M. S. Triantafyllou, and G. E. Karniadakis, " Deep learning of
vortex-induced vibrations, " J. Fluid Mech., vol. 861, pp. 119-137, Dec. 2018. doi: 10.1017/
jfm.2018.872.
[30] M. Raissi, A. Yazdani, and G. E. Karniadakis, " Hidden f luid mechanics: Learning velocity and pressure fields from f low visualizations, " Science, vol. 367, no. 6481,
pp. 1026-1030, Jan. 2020. doi: 10.1126/science.aaw4741.
[31] G. Kissas, Y. Yang, E. Hwuang, W. R. Witschey, J. A. Detre, and P. Perdikaris, " Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from
non-invasive 4d flow MRI data using physics-informed neural networks, " Comput. Methods
Appl. Mechan. Eng., vol. 358, p. 112,623, Jan. 2020. doi: 10.1016/j.cma.2019.112623.
[32] Y.-S. Ong and A. Gupta, " AIR5: Five pillars of artificial intelligence research, " IEEE
Trans. Emerg. Topics Computat. Intell., vol. 3, no. 5, pp. 411-415, Oct. 2019. doi: 10.1109/
TETCI.2019.2928344.
[33] K. O. Stanley, J. Clune, J. Lehman, and R. Miikkulainen, " Designing neural networks through neuroevolution, " Nat. Mach. Intell., vol. 1, no. 1, pp. 24-35, Jan. 2019. doi:
10.1038/s42256-018-0006-z.
[34] J.-B. Mouret and S. Doncieux, " Encouraging behavioral diversity in evolutionary
robotics: An empirical study, " Evol. Computat., vol. 20, no. 1, pp. 91-133, Mar. 2012. doi:
10.1162/EVCO_a_00048.
30
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2021
[35] X. Zhang, J. Clune, and K. O. Stanley. " On the relationship between the openai
evolution strategy and stochastic gradient descent. "
[36] J. Lehman, J. Chen, J. Clune, and K. O. Stanley. " Es is more than just a traditional
finite-difference approximator. "
[37] D. Wierstra, T. Schaul, T. Glasmachers, Y. Sun, J. Peters, and J. Schmidhuber, " Natural evolution strategies, " J. Mach. Learn. Res., vol. 15, no. 27, pp. 949-980, 2014. [Online].
Available: http://jmlr.org/papers/v15/wierstra14a.html
[38] A. Gupta, Y.-S. Ong, and L. Feng, " Insights on transfer optimization: Because experience is the best teacher, " IEEE Trans. Emerg. Topics Computat. Intell., vol. 2, no. 1,
pp. 51-64, Feb. 2018. doi: 10.1109/TETCI.2017.2769104.
[39] B. Da, A. Gupta, and Y.-S. Ong, " Curbing negative inf luences online for seamless
transfer evolutionary optimization, " IEEE Trans. Cybern., vol. 49, no. 12, pp. 4365-4378,
Dec. 2019. doi: 10.1109/TCYB.2018.2864345.
[40] B. Da, " Methods in multi-source data-driven transfer optimization, " Ph.D. dissertation, Nanyang Technol. Univ., 2019. [Online]. Available: https://hdl.handle.net/
10356/136964
[41] N. Hansen and A. Ostermeier, " Completely derandomized self-adaptation in evolution strategies, " Evol. Computat., vol. 9, no. 2, pp. 159-195, June 2001. doi: 10.1162/
106365601750190398.
[42] T. Salimans, J. Ho, X. Chen, S. Sidor, and I. Sutskever. " Evolution strategies as a
scalable alternative to reinforcement learning. "
[43] W. E and B. Yu, " The deep ritz method: A deep learning-based numerical algorithm
for solving variational problems, " Commun. Math. Statist., vol. 6, no. 1, pp. 1-12, Feb.
2018. doi: 10.1007/s40304-018-0127-z.
[44] S. Goswami, C. Anitescu, S. Chakraborty, and T. Rabczuk, " Transfer learning enhanced physics informed neural network for phase-field modeling of fracture, " Theor.
Appl. Fract. Mechan., vol. 106, p. 102,447, Apr. 2020. doi: 10.1016/j.tafmec.2019.102447.
[45] A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind, " Automatic differentiation in machine learning: A survey, " J. Mach. Learn. Res., vol. 18, no. 153, pp. 1-43,
2018. [Online]. Available: http://jmlr.org/papers/v18/17-468.html
[46] C. Igel, " Neuroevolution for reinforcement learning using evolution strategies, " in
Proc. 2003 Congr. Evolut. Computat., 2003. doi: 10.1109/CEC.2003.1299414.
[47] P. Pagliuca and S. Nolfi, " Robust optimization through neuroevolution, " PLoS One,
vol. 14, no. 3, p. e0213193, Mar. 2019. doi: 10.1371/journal.pone.0213193.
[48] C. Colas, V. Madhavan, J. Huizinga, and J. Clune, " Scaling MAP-elites to deep
neuroevolution, " in Proc. 2020 Genetic Evolut. Computat Conf. - GECCO 20, ACM Press.
[49] T. Schaul, T. Glasmachers, and J. Schmidhuber, " High dimensions and heavy tails for
natural evolution strategies, " in Proc. 13th Annu. Conf. Genetic Evolut. Computat. - GECCO 11,
ACM Press, ACM Press, 2011. doi: 10.1145/2001576.2001692.
[50] T. Glasmachers, T. Schaul, S. Yi, D. Wierstra, and J. Schmidhuber, " Exponential natural evolution strategies, " in Proc. 12th Annu. Conf. Genetic Evolut. Computat. - GECCO
10, ACM Press, 2010. doi: 10.1145/1830483.1830557.
[51] D. P. Kingma and J. Ba, " Adam: A method for stochastic optimization, " 2014, arXiv:
1412.6980.
[52] A. Gupta, " Numerical modelling and optimization of non-isothermal, rigid tool liquid
composite moulding processes, " Ph.D. dissertation, The University of Auckland, 2013.
[53] H. Okubo and M. Hubbard, " Identification of basketball parameters for a simulation
model, " Procedia Eng., vol. 2, no. 2, pp. 3281-3286, June 2010. doi: 10.1016/j.proeng.
2010.04.145.
[54] H. Hofmann, H. Wickham, and K. Kafadar, " Letter-value plots: Boxplots for large
data, " J. Computat. Graph. Statist., vol. 26, no. 3, pp. 469-477, July 2017. doi: 10.1080/
10618600.2017.1305277.
[55] U. Frisch and J. Bec, " Burgulence. "
[56] D. J. Korteweg and G. de Vries, " XLI. on the change of form of long waves advancing
in a rectangular canal, and on a new type of long stationary waves, " London, Edinburgh,
Dublin Philosoph. Mag. J. Sci., vol. 39, no. 240, pp. 422-443, May 1895. doi: 10.1080/
14786449508620739.
[57] Y. Yuan et al., " Machine discovery of partial differential equations from spatiotemporal data. "
[58] K. O. Stanley and R. Miikkulainen, " Evolving neural networks through augmenting topologies, " Evol. Computat., vol. 10, no. 2, pp. 99-127, June 2002. doi: 10.1162/ 1063656023
20169811.
[59] C. Fernando et al., " Pathnet: Evolution channels gradient descent in super neural
networks. "
[60] L. Bai, Y.-S. Ong, T. He, and A. Gupta, " Multi-task gradient descent for multi-task learning, " Meme. Comput., vol. 12, no. 4, pp. 355-369, Oct. 2020. doi: 10.1007/s12293-020-00316-3.
[61] T. P. Dinh, B. H. T. Thanh, T. T. Ba, and L. N. Binh, " Multifactorial evolutionary
algorithm for solving clustered tree problems: Competition among Cayley codes, " Memetic
Comput., vol. 12, no. 3, pp. 185-217, Aug. 2020. doi: 10.1007/s12293-020-00309-2.
[62] O. Abramovich and A. Moshaiov, " Multi-objective topology and weight evolution of neuro-controllers, " in Proc. IEEE Congr. Evolut. Computat. (CEC), July 2016. doi:
10.1109/CEC.2016.7743857.
[63] S. Künzel and S. Meyer-Nieberg, " Evolving artificial neural networks for multi-objective
tasks, " in Applications of Evolutionary Computation. Springer-Verlag, 2018, pp. 671-686.
[64] A. D. Jagtap, E. Kharazmi, and G. E. Karniadakis, " Conservative physics-informed
neural networks on discrete domains for conservation laws: Applications to forward and
inverse problems, " Comput. Methods Appl. Mechan. Eng., vol. 365, p. 113,028, June 2020.
doi: 10.1016/j.cma.2020.113028.
[65] K. Li, K. Tang, T. Wu, and Q. Liao, " D3M: A deep domain decomposition method
for partial differential equations, " IEEE Access, vol. 8, pp. 5283-5294, 2020. doi: 10.1109/
ACCESS.2019.2957200.
http://www.jmlr.org/papers/v15/wierstra14a.html
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IEEE Computational Intelligence Magazine - May 2021
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