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The advent of scientific machine learning (SciML) has opened up a new field with many promises and challenges in the field of simulation science by developing approaches at the interface of physics- and data-based modelling.
A hybrid neural network-first principles approach to process modeling
Psichogios, D. C. and Ungar, L. H · 1992
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Artificial neural networks for solving ordinary and partial differential equations
Lagaris, I. E., Likas, A., and Fotiadis, D. I · 1998
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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A deep learning based approach to reduced order modeling for turbulent flow control using lstm neural networks, 2018
Mohan, A. T. and Gaitonde, D. V · 2018
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Raissi, M., Perdikaris, P., and Karniadakis, G · 2018
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Adversarial uncertainty quantification in physics-informed neural networks
Yang, Y. and Perdikaris, P · 2019
Cited alongside, same era.
Solving parametric pde problems with artificial neural networks
Khoo, Y., LU, J., and YING, L · 2020
Cited alongside, same era.
Fourier neural operator for parametric partial differential equations, 2020
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2020
Cited alongside, same era.
Cfdnet: A deep learning-based accelerator for fluid simulations
Obiols-Sales, O., Vishnu, A., Malaya, N., and Chandramowliswharan, A · 2020
Later among the works it cites.
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Raissi, M., Yazdani, A., and Karniadakis, G. E · 2020
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On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type pdes, 2020
Shin, Y., Darbon, J., and Karniadakis, G. E · 2020
Later among the works it cites.
Physics-informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems
Tartakovsky, A. M., Marrero, C. O., Perdikaris, P., Tartakovsky, G. D., and Barajas-Solano, D · 2020
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Understanding and mitigating gradient pathologies in physics-informed neural networks, 2020
Wang, S., Teng, Y., and Perdikaris, P · 2020
Later among the works it cites.
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