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E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019). Article ADS MathSciNet MATH Google Scholar Schmidt, M. & Lipson, H. Distilling free-form natural ...
‘Intuitive physics’ enables our pragmatic engagement with the physical world and forms a key component of ‘common sense’ aspects of thought. Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to ev
Developing pre-service mathematics teachers’ metacognitive thinking for learning and teaching with mobile technology. Eurasia Proc. Educ. Soc. Sci. 2018, 9, 239–249. [Google Scholar] Damopolii, I.; Kurniadi, B. Training students metacognitive skill using mobile learning. J. Physics: Conf. ...
However, conventional data-driven machine learning algorithms require heavy inputs of large labeled data, which is computationally expensive for complex and multi-physics problems. In this paper, we proposed a data-free, physics-driven deep learning approach to solve various low-speed flow problems ...
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