Asher, M. J., B. F. W. Croke, A. J. Jakeman, and L. J. M. Peeters. 2015.
“A Review of Surrogate Models and Their Application to Groundwater Modeling.” Water Resources Research 51 (8): 5957–73.
https://doi.org/10.1002/2015WR016967.
Cui, Tao, Luk Peeters, Dan Pagendam, Trevor Pickett, Huidong Jin, Russell S. Crosbie, Matthias Raiber, David W. Rassam, and Mat Gilfedder. 2018.
“Emulator-Enabled Approximate Bayesian Computation (ABC) and Uncertainty Analysis for Computationally Expensive Groundwater Models.” Journal of Hydrology 564 (September): 191–207.
https://doi.org/10.1016/j.jhydrol.2018.07.005.
Gladish, Daniel W., Daniel E. Pagendam, Luk J. M. Peeters, Petra M. Kuhnert, and Jai Vaze. 2018.
“Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models.” Journal of Agricultural, Biological and Environmental Statistics 23 (1): 39–62.
https://doi.org/10.1007/s13253-017-0308-3.
Goldstein, Evan B., and Giovanni Coco. 2015.
“Machine Learning Components in Deterministic Models: Hybrid Synergy in the Age of Data.” Frontiers in Environmental Science 3 (April).
https://doi.org/10.3389/fenvs.2015.00033.
Jarvenpaa, Marko, Aki Vehtari, and Pekka Marttinen. 2020.
“Batch Simulations and Uncertainty Quantification in Gaussian Process Surrogate Approximate Bayesian Computation.” In
Conference on Uncertainty in Artificial Intelligence, 779–88.
PMLR.
http://proceedings.mlr.press/v124/jarvenpaa20a.html.
Kasim, M. F., D. Watson-Parris, L. Deaconu, S. Oliver, P. Hatfield, D. H. Froula, G. Gregori, et al. 2020.
“Up to Two Billion Times Acceleration of Scientific Simulations with Deep Neural Architecture Search.” January 17, 2020.
http://arxiv.org/abs/2001.08055.
Kononenko, O., and I. Kononenko. 2018.
“Machine Learning and Finite Element Method for Physical Systems Modeling.” March 16, 2018.
http://arxiv.org/abs/1801.07337.
Laloy, Eric, and Diederik Jacques. 2019.
“Emulation of CPU-Demanding Reactive Transport Models: A Comparison of Gaussian Processes, Polynomial Chaos Expansion, and Deep Neural Networks.” Computational Geosciences 23 (5): 1193–1215.
https://doi.org/10.1007/s10596-019-09875-y.
Lu, Dan, and Daniel Ricciuto. 2019.
“Efficient Surrogate Modeling Methods for Large-Scale Earth System Models Based on Machine-Learning Techniques.” Geoscientific Model Development 12 (5): 1791–1807.
https://doi.org/10.5194/gmd-12-1791-2019.
Merwe, Rudolph van der, Todd K. Leen, Zhengdong Lu, Sergey Frolov, and Antonio M. Baptista. 2007.
“Fast Neural Network Surrogates for Very High Dimensional Physics-Based Models in Computational Oceanography.” Neural Networks, Computational
Intelligence in
Earth and
Environmental Sciences, 20 (4): 462–78.
https://doi.org/10.1016/j.neunet.2007.04.023.
Mo, Shaoxing, Dan Lu, Xiaoqing Shi, Guannan Zhang, Ming Ye, Jianfeng Wu, and Jichun Wu. 2017.
“A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling.” Water Resources Research 53 (12): 10802–23.
https://doi.org/10.1002/2017WR021622.
O’Hagan, A. 1978.
“Curve Fitting and Optimal Design for Prediction.” Journal of the Royal Statistical Society: Series B (Methodological) 40 (1): 1–24.
https://doi.org/10.1111/j.2517-6161.1978.tb01643.x.
———. 2006.
“Bayesian Analysis of Computer Code Outputs: A Tutorial.” Reliability Engineering & System Safety, The
Fourth International Conference on
Sensitivity Analysis of
Model Output (
SAMO 2004), 91 (10): 1290–300.
https://doi.org/10.1016/j.ress.2005.11.025.
Oakley, Jeremy E., and Benjamin D. Youngman. 2017.
“Calibration of Stochastic Computer Simulators Using Likelihood Emulation.” Technometrics 59 (1): 80–92.
https://doi.org/10.1080/00401706.2015.1125391.
Paleyes, Andrei, Mark Pullin, Maren Mahsereci, Neil Lawrence, and Javier Gonzalez. 2019.
“Emulation of Physical Processes with Emukit.” In
Advances In Neural Information Processing Systems, 8.
https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_113.pdf.
Plumlee, Matthew. 2017.
“Bayesian Calibration of Inexact Computer Models.” Journal of the American Statistical Association 112 (519): 1274–85.
https://doi.org/10.1080/01621459.2016.1211016.
Razavi, Saman, Bryan A. Tolson, and Donald H. Burn. 2012.
“Review of Surrogate Modeling in Water Resources.” Water Resources Research 48 (7).
https://doi.org/10.1029/2011WR011527.
Rueden, Laura von, Sebastian Mayer, Rafet Sifa, Christian Bauckhage, and Jochen Garcke. 2020.
“Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions.” In
Advances in Intelligent Data Analysis XVIII, edited by Michael R. Berthold, Ad Feelders, and Georg Krempl, 12080:548–60. Lecture
Notes in
Computer Science.
Cham:
Springer International Publishing.
https://doi.org/10.1007/978-3-030-44584-3_43.
Siade, Adam J., Tao Cui, Robert N. Karelse, and Clive Hampton. 2020.
“Reduced‐Dimensional Gaussian Process Machine Learning for Groundwater Allocation Planning Using Swarm Theory.” Water Resources Research 56 (3).
https://doi.org/10.1029/2019WR026061.
Tait, Daniel J., and Theodoros Damoulas. 2020.
“Variational Autoencoding of PDE Inverse Problems.” June 28, 2020.
http://arxiv.org/abs/2006.15641.
Teweldebrhan, Aynom T., Thomas V. Schuler, John F. Burkhart, and Morten Hjorth-Jensen. 2020.
“Coupled Machine Learning and the Limits of Acceptability Approach Applied in Parameter Identification for a Distributed Hydrological Model.” Hydrology and Earth System Sciences 24 (9): 4641–58.
https://doi.org/10.5194/hess-24-4641-2020.
Tompson, Jonathan, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin. 2017.
“Accelerating Eulerian Fluid Simulation with Convolutional Networks.” In
Proceedings of the 34th International Conference on Machine Learning - Volume 70, 3424–33.
ICML’17.
Sydney, NSW, Australia:
JMLR.org.
http://proceedings.mlr.press/v70/tompson17a.html.
Vernon, Ian, Michael Goldstein, and Richard Bower. 2014.
“Galaxy Formation: Bayesian History Matching for the Observable Universe.” Statistical Science 29 (1): 81–90.
https://doi.org/10.1214/12-STS412.
White, Jeremy T., Michael N. Fienen, and John E. Doherty. 2016.
“A Python Framework for Environmental Model Uncertainty Analysis.” Environmental Modelling & Software 85 (November): 217–28.
https://doi.org/10.1016/j.envsoft.2016.08.017.
Yashchuk, Ivan. 2020.
“Bringing PDEs to JAX with Forward and Reverse Modes Automatic Differentiation.” In.
https://openreview.net/forum?id=nEPNoiGsU3.
Yu, Xiayang, Tao Cui, J. Sreekanth, Stephane Mangeon, Rebecca Doble, Pei Xin, David Rassam, and Mat Gilfedder. 2020.
“Deep Learning Emulators for Groundwater Contaminant Transport Modelling.” Journal of Hydrology, August, 125351.
https://doi.org/10.1016/j.jhydrol.2020.125351.
Zhu, Yinhao, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, and Paris Perdikaris. 2019.
“Physics-Constrained Deep Learning for High-Dimensional Surrogate Modeling and Uncertainty Quantification Without Labeled Data.” Journal of Computational Physics 394 (October): 56–81.
https://doi.org/10.1016/j.jcp.2019.05.024.
No comments yet!