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Physics-based models of dynamical systems are often used to study engineering and environmental systems.
Some evaluations of drag and bulk transfer coefficients over water bodies of different sizes
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Physics constrained nonlinear regression models for time series
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Constrained semi-supervised learning Using attributes and comparative attributes. In Proceedings of the 12th European Conference on Computer Vision - Volume Part III (ECCV’12) . Springer-Verlag, Berlin, Heidelberg, 369–383
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Detecting causality in complex ecosystems
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Fragmentation and thermal risks from climate change interact to affect persistence of native trout in the Colorado River basin
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Debates—The future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science
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GLM-General Lake Model: Model overview and user information
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Adam: A method for stochastic optimization
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Debates—The future of hydrological sciences: A (common) path forward? One water. One world. Many climes. Many souls
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The parable of Google Flu: traps in big data analysis
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Debates—The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph
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From group to individual labels using deep features. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 597–606
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Data-driven methods to improve baseflow prediction of a regional groundwater model
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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Deep learning for computational chemistry
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Incorporating prior domain knowledge into deep neural networks. In IEEE Big Data . IEEE
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Physics-based generative adversarial models for image restoration and beyond
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Long-time predictive modeling of nonlinear dynamical systems using neural networks
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Deep hidden physics models: Deep learning of nonlinear partial differential equations
Maziar Raissi. 2018 · 2018
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Multistep neural networks for data-driven discovery of nonlinear dynamical systems
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Cited alongside, same era.
Hybrid modeling and prediction of dynamical systems
Franz Hamilton, Alun L Lloyd, and Kevin B Flores. 2017 · 2017
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Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset
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DR-RNN: A deep residual recurrent neural network for model reduction
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Theory-guided data science: A new paradigm for scientific discovery from data
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Cited alongside, same era.
Physics-guided neural networks (PGNN): An application in lake temperature modeling
Anuj Karpatne, William Watkins, Jordan Read, and Vipin Kumar. 2017b · 2017
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FlareNet: A Deep Learning Framework for Solar Phenomena Prediction. In Workshop on Deep Learning for Physical Sciences (DLPS 2017), NIPS 2017
Sean McGregor, Dattaraj Dhuri, Anamaria Berea, and Andrés Muñoz-Jaramillo. [n. d.] · 2017
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Accelerating high-energy physics exploration with deep learning. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact . ACM, 37
Dave Ojika, Darin Acosta, Ann Gordon-Ross, Andrew Carnes, and Sergei Gleyzer. 2017 · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. 2018 · 2018
Later among the works it cites.
Maziar Raissi, Alireza Yazdani, and George Em Karniadakis. 2018 · 2018
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Learning with Weak Supervision from Physics and Data-Driven Constraints
Hongyu Ren et al · 2018
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Machine learning closures for model order reduction of thermal fluids
O San and R Maulik. 2018 · 2018
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A domain guided CNN architecture for predicting age from structural brain images
Pascal Sturmfels, Saige Rutherford, Mike Angstadt, Mark Peterson, Chandra Sripada, and Jenna Wiens. 2018 · 2018
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Learning parameters and constitutive relationships with physics informed deep neural networks
Alexandre M Tartakovsky, Carlos Ortiz Marrero, D Tartakovsky, and David Barajas-Solano. 2018 · 2018
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Data-assisted reduced-order modeling of extreme events in complex dynamical systems
Zhong Yi Wan, Pantelis Vlachas, Petros Koumoutsakos, and Themistoklis Sapsis. 2018 · 2018
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The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Kun Yao, John E Herr, David W Toth, Ryker Mckintyre, and John Parkhill. 2018 · 2018
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Achieving conservation of energy in neural network emulators for climate modeling
T Beucler, S Rasp, M Pritchard, and P Gentine. 2019 · 2019
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A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)
Matthew R Hipsey, Louise C Bruce, Casper Boon, Brendan Busch, Cayelan C Carey, David P Hamilton, Paul C Hanson, Jordan S Read, Eduardo De Sousa, Michael Weber, et al · 2019
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Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles. In Proceedings of the 2019 SIAM International Conference on Data Mining . SIAM, 558–566
Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, and Vipin Kumar. 2019 · 2019
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Heavy rain image restoration: Integrating physics model and conditional adversarial learning
Ruotent Li, Loong Fah Cheong, and Robby T Tan. 2019 · 2019
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Process-guided deep learning predictions of lake water temperature
Jordan S Read, Xiaowei Jia, Jared Willard, Alison P Appling, Jacob A Zwart, Samantha K Oliver, Anuj Karpatne, Gretchen JA Hansen, Paul C Hanson, William Watkins, et al · 2019
Later among the works it cites.
Predicting lake surface water phosphorus dynamics using process-guided machine learning
Paul C Hanson, Aviah B Stillman, Xiaowei Jia, Anuj Karpatne, Hilary A Dugan, Cayelan C Carey, Joseph Stachelek, Nicole K Ward, Yu Zhang, Jordan S Read, et al · 2020
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Integrating physics-based modeling with machine learning: A survey
Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2020 · 2020
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