A Deep Learning Approach for the Obstacle Problem
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-6887-6_16
Reference25 articles.
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2. Burman E, Hansbo P, Larson MG, Stenberg R (2017) Galerkin least squares finite element method for the obstacle problem. Comput Methods Appl Mech Eng 313:362–374. https://doi.org/10.1016/j.cma.2016.09.025
3. Calder J, Yezzi A (2019) PDE acceleration: a convergence rate analysis and applications to obstacle problems. Res Math Sci 6(4):35. https://doi.org/10.1007/s40687-019-0197-x
4. Dabaghi J, Martin V, Vohralík M (2020) A posteriori estimates distinguishing the error components and adaptive stopping criteria for numerical approximations of parabolic variational inequalities. Comput Methods Appl Mech Eng 367:113105. https://doi.org/10.1016/j.cma.2020.113105
5. Doukhovni I, Givoli D (1996) Quadratic programming algorithms for obstacle problems. Commun Numer Methods Eng 12(4):249-256. doi.org/10.1002/(SICI)1099-0887(199604)12:4$$<$$249::AID-CNM976$$>$$3.0.CO;2-6
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