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Becker, S., Cheridito, P., Jentzen, A. & Welti, T. (2021) Solving high-dimensional optimal stopping problems using deep learning. European Journal of Applied Mathematics, 32, 470–514.Google Scholar
[2]
Chen, X., Duan, J. & Karniadakis, G. E. (2021) Learning and meta-learning of stochastic advection-diffusion-reaction systems from sparse measurements. European Journal of Applied Mathematics, 32, 397–420.Google Scholar
[3]
Gin, C., Lusch, B., Brunton, S. C. & Kutz, J. N. (2021) Deep learning models for global coordinate transformations that linearize PDEs. European Journal of Applied Mathematics, 32, 515–539.Google Scholar
[4]
Khoo, Y., Lu, J. % Ying, L. (2021) Solving parametric PDE problems with artificial neural networks. European Journal of Applied Mathematics, 32, 421–435.Google Scholar
[5]
Lye, K. O., Mishra, S. & Molinaro, R. (2021) A Multi-level procedure for enhancing accuracy of machine learning algorithms. European Journal of Applied Mathematics, 32, 436–469.Google Scholar
[6]
Savarino, F. & Schnörr, C. (2021) Continuous-domain assignment flows. European Journal of Applied Mathematics, 32, 570–597.Google Scholar
[7]
Wang, B. & Osher, S. J. (2021) Graph interpolating activation improves both natural and robust accuracies in data- efficient deep learning. European Journal of Applied Mathematics, 32, 540–569.Google Scholar