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Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inform neural networks, with the need of less data for training a reliable model.
R. Byrd, P. Lu, J. Nocedal, C. Zhu, A limited memory algorithm for bound constrained optimization, SIAM Journal on Scientific Computing 16 (5) (1995) 1190–1208
1995
Earlier work this paper cites.
I. E. Lagaris, A. Likas, D. I. Fotiadis, Artificial neural networks for solving ordinary and partial differential equations, IEEE transactions on neural networks 9 (5) (1998) 987–1000
1998
Earlier work this paper cites.
I. E. Lagaris, A. C. Likas, D. G. Papageorgiou, Neural-network methods for boundary value problems with irregular boundaries, IEEE Transactions on Neural Networks 11 (5) (2000) 1041–1049
2000
Earlier work this paper cites.
C. Yang, X. Yang, X. Xiao, Data-driven projection method in fluid simulation, Computer Animation and Virtual Worlds 27 (3-4) (2016) 415–424
2016
Earlier work this paper cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., Tensorflow: A system for large-scale machine learning, in: 12th USENIX Symposium on Operating Systems Design and Implementation, 2016, pp. 265–283
2016
Earlier work this paper cites.
J. Tompson, K. Schlachter, P. Sprechmann, K. Perlin, Accelerating eulerian fluid simulation with convolutional networks, in: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, 2017, pp. 3424–3433
2017
Earlier work this paper cites.
S. Lee, N. Baker, Basic research needs for scientific machine learning: Core technologies for artificial intelligence, Tech. rep., USDOE Office of Science (SC)(United States) (2018)
2018
Cited alongside, same era.
ANSYS Fluent 18.1, ANSYS FLUENT Theory Guide, ANSYS Inc., PA, USA, 2018
2018
Cited alongside, same era.
R. Zhang, Z. Chen, S. Chen, J. Zheng, O. Büyüköztürk, H. Sun, Deep long short-term memory networks for nonlinear structural seismic response prediction, Computers & Structures 220 (2019) 55 – 68
2019
Cited alongside, same era.
M. Raissi, P. Perdikaris, G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics 378 (2019) 686–707
2019
Cited alongside, same era.
N. Geneva, N. Zabaras, Quantifying model form uncertainty in reynolds-averaged turbulence models with bayesian deep neural networks, Journal of Computational Physics 383 (2019) 125–147
2019
Later among the works it cites.
L. Sun, H. Gao, S. Pan, J.-X. Wang, Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data, Computer Methods in Applied Mechanics and Engineering (2019) 112732
2019
Later among the works it cites.
Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, P. Perdikaris, Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data, Journal of Computational Physics 394 (2019) 56–81
2019
Later among the works it cites.
G. Kissas, Y. Yang, E. Hwuang, W. R. Witschey, J. A. Detre, P. Perdikaris, Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4d flow mri data using physics-informed neural networks, Computer Methods in Applied Mechanics and Engineering 358 (2020) 112623
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2019
Cited alongside, same era.
D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980
Cited in the paper.
2020
Closest in time.