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Physics-informed Machine Learning has recently become attractive for learning physical parameters and features from simulation and observation data.
G. H. Leavesley and L. G. Stannard, “The precipitation-runoff modeling system-PRMS,” Computer models of watershed hydrology , pp. 281–310, 1995
1995
Earlier work this paper cites.
R. Eymard, T. Gallouët, and R. Herbin, “Finite Volume Methods,” Handbook of Numerical Analysis , vol. 7, pp. 713–1018, 2000
2000
Earlier work this paper cites.
T. J. R. Hughes, G. Engel, L. Mazzei, and M. G. Larson, “The continuous Galerkin method is locally conservative,” Journal of Computational Physics , vol. 163, pp. 467–488, 2000
2000
Earlier work this paper cites.
M. B. Giles, M. C. Duta, J.-D. Muller, and N. A. Pierce, “Algorithm developments for discrete adjoint methods,” AIAA journal , vol. 41, pp. 198–205, 2003
2003
Earlier work this paper cites.
O. C. Zienkiewicz, R. L. Taylor, and J. Z. Zhu, The finite element method: Its basis and fundamentals , 2005
2005
Earlier work this paper cites.
H. Jasak, A. Jemcov, and Z. Tukovic, “OpenFOAM: A C++ library for complex physics simulations,” in International workshop on coupled methods in numerical dynamics , vol. 1000, 2007, pp. 1–20
2007
Earlier work this paper cites.
S. K. Nadarajah and A. Jameson, “Optimum shape design for unsteady flows with time-accurate continuous and discrete adjoint method,” AIAA journal , vol. 45, pp. 1478–1491, 2007
2007
Earlier work this paper cites.
A. Cichocki, R. Zdunek, A. H. Phan, and S.-I. Amari, Nonnegative matrix and tensor factorizations: Applications to exploratory multi-way data analysis and blind source separation . John Wiley & Sons, 2009
2009
Earlier work this paper cites.
M. P. Rumpfkeil and D. W. Zingg, “The optimal control of unsteady flows with a discrete adjoint method,” Optimization and Engineering , vol. 11, pp. 5–22, 2010
2010
Earlier work this paper cites.
J. G. Arnold, D. N. Moriasi, P. W. Gassman, K. C. Abbaspour, M. J. White, R. Srinivasan, C. Santhi, R. D. Harmel, A. V. Griensven, and M. W. Van Liew, “SWAT: Model use, calibration, and validation,” Transactions of the ASABE , vol. 55, pp. 1491–1508, 2012
2012
Earlier work this paper cites.
G. Hammond, P. Lichtner, and R. Mills, “Evaluating the performance of parallel subsurface simulators: An illustrative example with PFLOTRAN,” Water Resources Research , vol. 50, no. 1, pp. 208–228, 2014
2014
Earlier work this paper cites.
P. C. Lichtner, G. E. Hammond, C. Lu, S. Karra, G. Bisht, B. Andre, R. Mills, and J. Kumar, “Pflotran user manual: A massively parallel reactive flow and transport model for describing surface and subsurface processes,” Tech. Rep., 2015
2015
Earlier work this paper cites.
M. Towara, M. Schanen, and U. Naumann, “MPI-parallel discrete adjoint OpenFOAM,” Procedia Computer Science , vol. 51, pp. 19–28, 2015
2015
Earlier work this paper cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “Tensorflow: A system for large-scale machine learning,” in 12th { \{ USENIX } \} symposium on operating systems design and implementation ( { \{ OSDI } \} 16) , 2016, pp. 265–283
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Karpatne, G. Atluri, J. H. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, and V. Kumar, “Theory-guided data science: A new paradigm for scientific discovery from data,” IEEE Transactions on knowledge and data engineering , vol. 29, pp. 2318–2331, 2017
2017
Earlier work this paper cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Earlier work this paper cites.
P. Messina, “The exascale computing project,” Computing in Science & Engineering , vol. 19, pp. 63–67, 2017
2017
Earlier work this paper cites.
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller, “Explaining nonlinear classification decisions with deep Taylor decomposition,” Pattern Recognition , vol. 65, pp. 211–222, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
K. Sampson and D. Gochis, “RF Hydro GIS Pre-Processing Tools, Version 5.0, Documentation,” Boulder, CO: National Center for Atmospheric Research, Research Applications Laboratory , 2018
2018
Cited alongside, same era.
F. K. Došilović, M. Brčić, and N. Hlupić, “Explainable artificial intelligence: A survey,” in 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO) . IEEE, 2018, pp. 0210–0215
2018
Cited alongside, same era.
M. K. Mudunuru, D. O’Malley, S. Srinivasan, J. D. Hyman, M. R. Sweeney, L. Frash, B. Carey, M. R. Gross, N. J. Welch, S. Karra, V. V. Vesselinov, Q. Kang, H. Xu, R. J. Pawar, T. Carr, L. Li, G. D. Guthrie, and H. S. Viswanathan, “Physics-informed machine learning for real-time reservoir management.” in AAAI Spring Symposium: MLPS , 2020
2020
Later among the works it cites.
J. E. Santos, M. Mehana, H. Wu, M. Prodanovic, Q. Kang, N. Lubbers, H. Viswanathan, and M. J. Pyrcz, “Modeling nanoconfinement effects using active learning,” The Journal of Physical Chemistry C , vol. 124, pp. 22 200–22 211, 2020
2020
Later among the works it cites.
E. E. Knight, E. Rougier, Z. Lei, B. Euser, V. Chau, S. H. Boyce, K. Gao, K. Okubo, and M. Froment, “HOSS: An implementation of the combined finite-discrete element method,” Computational Particle Mechanics , vol. 7, pp. 765–787, 2020
2020
Later among the works it cites.
O. Fuks and H. A. Tchelepi, “Limitations of physics informed machine learning for nonlinear two-phase transport in porous media,” Journal of Machine Learning for Modeling and Computing , vol. 1, 2020
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A. Adadi and M. Berrada, “Peeking inside the black-box: A survey on explainable artificial intelligence (XAI),” IEEE access , vol. 6, pp. 52 138–52 160, 2018
2018
Cited alongside, same era.
V. Vesselinov, M. Mudunuru, S. Karra, D. O’Malley, and B. Alexandrov, “Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing,” Journal of Computational Physics , vol. 395, pp. 85–104, 2019
2019
Cited alongside, same era.
M. Raissi, P. Perdikaris, and 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 , vol. 378, pp. 686–707, 2019
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, and L. Antiga, “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, pp. 8026–8037, 2019
2019
Cited alongside, same era.
J.-C. Golaz, P. M. Caldwell, L. P. Van R., M. R. Petersen, Q. Tang, J. D. Wolfe, G. Abeshu, V. Anantharaj, X. S. Asay-D., and D. C. Bader, “The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution,” Journal of Advances in Modeling Earth Systems , vol. 11, pp. 2089–2129, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. Stevens, J. Ramprakash, P. Messina, M. Papka, and K. Riley, “Aurora: Argonne’s next-generation exascale supercomputer,” ANL (Argonne National Laboratory (ANL), Argonne, IL (United States)), Tech. Rep., 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
C. Yang and J. Deslippe, “Accelerate Science on Perlmutter with NERSC,” Bulletin of the American Physical Society , vol. 65, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Sundararajan and A. Najmi, “The many Shapley values for model explanation,” in International Conference on Machine Learning . PMLR, 2020, pp. 9269–9278
2020
Later among the works it cites.
Y. Zhang, Z.-Q. J. Xu, T. Luo, and Z. Ma, “A type of generalization error induced by initialization in deep neural networks,” in Mathematical and Scientific Machine Learning . PMLR, 2020, pp. 144–164
2020
Later among the works it cites.
D. R. Harp, D. O’Malley, B. Yan, and R. Pawar, “On the feasibility of using physics-informed machine learning for underground reservoir pressure management,” Expert Systems with Applications , vol. 178, p. 115006, 2021
2021
Closest in time.
K. Kashinath, M. Mustafa, A. Albert, J. L. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, R. Wang, A. Chattopadhyay, and A. Singh, “Physics-informed machine learning: Case studies for weather and climate modelling,” Philosophical Transactions of the Royal Society A , vol. 379, p. 20200093, 2021
2021
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2021
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L. Lu, X. Meng, Z. Mao, and G. E. Karniadakis, “Deepxde: A deep learning library for solving differential equations,” SIAM Review , vol. 63, no. 1, pp. 208–228, 2021
2021
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(2021) NERSC – National Energy Research Scientific Computing Center. Accessed on: 2021-09-03. [Online]. Available: https://www.nersc.gov/
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(2021) OLCF – Oak Ridge Leadership Computing Facility. Accessed on: 2021-09-03. [Online]. Available: https://www.olcf.ornl.gov/
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(2021) ALCF – Argonne Leadership Computing Facility. Accessed on: 2021-09-03. [Online]. Available: https://www.alcf.anl.gov/
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