标题/作者	年份
Digital twin: Values, challenges and enablers from a modeling perspective A Rasheed, O San, T Kvamsdal Ieee Access 8, 21980-22012	2020
Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems L Von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ... IEEE Transactions on Knowledge and Data Engineering 35 (1), 614-633	2021
Lift & learn: Physics-informed machine learning for large-scale nonlinear dynamical systems E Qian, B Kramer, B Peherstorfer, K Willcox Physica D: Nonlinear Phenomena 406, 132401	2020
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders R Maulik, B Lusch, P Balaprakash Physics of Fluids 33 (3)	2021
Predicting CO2 plume migration in heterogeneous formations using conditional deep convolutional generative adversarial network Z Zhong, AY Sun, H Jeong Water Resources Research 55 (7), 5830-5851	2019
Digital twin: Values, challenges and enablers A Rasheed, O San, T Kvamsdal arXiv preprint arXiv:1910.01719	2019
Time-resolved turbulent velocity field reconstruction using a long short-term memory (LSTM)-based artificial intelligence framework Z Deng, Y Chen, Y Liu, KC Kim Physics of Fluids 31 (7)	2019
Learning physics-based reduced-order models for a single-injector combustion process R Swischuk, B Kramer, C Huang, K Willcox AIAA Journal 58 (6), 2658-2672	2020
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues SE Ahmed, S Pawar, O San, A Rasheed, T Iliescu, BR Noack Physics of Fluids 33 (9)	2021
Thermal field prediction for laser scanning paths in laser aided additive manufacturing by physics-based machine learning K Ren, Y Chew, YF Zhang, JYH Fuh, GJ Bi Computer Methods in Applied Mechanics and Engineering 362, 112734	2020
Combining machine learning and simulation to a hybrid modelling approach: Current and future directions L von Rueden, S Mayer, R Sifa, C Bauckhage, J Garcke Advances in Intelligent Data Analysis XVIII: 18th International Symposium on …	2020
Data-driven recovery of hidden physics in reduced order modeling of fluid flows S Pawar, SE Ahmed, O San, A Rasheed Physics of Fluids 32 (3)	2020
Learning corrections for hyperelastic models from data D González, F Chinesta, E Cueto Frontiers in Materials 6, 14	2019
Physics guided machine learning using simplified theories S Pawar, O San, B Aksoylu, A Rasheed, T Kvamsdal Physics of Fluids 33 (1)	2021
Learning physics-based models from data: perspectives from inverse problems and model reduction O Ghattas, K Willcox Acta Numerica 30, 445-554	2021
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil SA Renganathan, R Maulik, V Rao Physics of Fluids 32 (4)	2020
Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm S Guo, M Agarwal, C Cooper, Q Tian, RX Gao, WG Grace, YB Guo Journal of Manufacturing Systems 62, 145-163	2022
A long short-term memory embedding for hybrid uplifted reduced order models SE Ahmed, O San, A Rasheed, T Iliescu Physica D: Nonlinear Phenomena 409, 132471	2020
Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework M Mahmoudabadbozchelou, M Caggioni, S Shahsavari, WH Hartt, ... Journal of Rheology 65 (2), 179-198	2021
Higher order dynamic mode decomposition and its applications JM Vega, S Le Clainche Academic Press	2020

1 - 20

计算机程序会自动估算并确定日期和引用次数。