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dr. M. Guo (Mengwu)

Universitair docent

Publicaties

Recent
Conti, P. , Guo, M., Manzoni, A., & Hesthaven, J. S. (2023). Multi-fidelity surrogate modeling using long short-term memory networks. Computer methods in applied mechanics and engineering, 404, [115811]. https://doi.org/10.1016/j.cma.2022.115811
Guo, M., McQuarrie, S. A., & Willcox, K. E. (2022). Bayesian operator inference for data-driven reduced-order modeling. Computer methods in applied mechanics and engineering, 402, [115336]. https://doi.org/10.1016/j.cma.2022.115336
Guo, M., Manzoni, A., Amendt, M., Conti, P., & Hesthaven, J. S. (2022). Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities. Computer methods in applied mechanics and engineering, 389, [114378]. https://doi.org/10.1016/j.cma.2021.114378
Guo, M. , & Brune, C. (2021). Uncertainty quantification for physics-informed deep learning. In W. H. A. Schilders (Ed.), Mathematics: Key Enabling Technology for Scientific Machine Learning (pp. 47-51) https://platformwiskunde.nl/wp-content/uploads/2021/11/Math_KET_SciML.pdf
Guo, M., McQuarrie, S. A., & Willcox, K. E. (Accepted/In press). Bayesian operator inference for the reduced order modeling of time-dependent problems. Abstract from SIMAI 2020+21, Italy.
Guo, M., Hesthaven, J. S., Kast, M., McQuarrie, S. A., & Willcox, K. E. (Accepted/In press). Bayesian methods for non-intrusive reduced order modeling. Abstract from Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering and Technology (MMLDT-CSET) Conference, San Diego, United States.
Guo, M., & Haghighat, E. (Accepted/In press). Bounding discretization errors of physics-informed neural network solutions in elasticity. Abstract from 16th U.S. National Congress on Computational Mechanics, United States.
Guo, M., McQuarrie, S. A., & Willcox, K. E. (2021). A Bayesian formulation of operator inference for non-intrusive reduced order modeling. Abstract from SIAM Conference on Computational Science and Engineering 2021, United States.
Bigoni, C. , Guo, M., & Hesthaven, J. S. (2021). Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling. In A. Cury, D. Ribeiro, F. Ubertini, & M. D. Todd (Eds.), Structural health monitoring based on data science techniques (pp. 185-205). (Structural Integrity (STIN); Vol. 21). Springer. https://doi.org/10.1007/978-3-030-81716-9_9
Kast, M. , Guo, M., & Hesthaven, J. S. (2020). A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems. Computer methods in applied mechanics and engineering, 364, [112947]. https://doi.org/10.1016/j.cma.2020.112947
Yu, J., Yan, C. , & Guo, M. (2019). Non-intrusive reduced-order modeling for fluid problems: A brief review. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(16), 5896-5912. https://doi.org/10.1177/0954410019890721
Zhang, Z. , Guo, M., & Hesthaven, J. S. (2019). Model order reduction for large-scale structures with local nonlinearities. Computer methods in applied mechanics and engineering, 353, 491-515. https://doi.org/10.1016/j.cma.2019.04.042
Guo, M., & Hesthaven, J. S. (2019). Data-driven reduced order modeling for time-dependent problems. Computer methods in applied mechanics and engineering, 345, 75-99. https://doi.org/10.1016/j.cma.2018.10.029
Guo, M., & Hesthaven, J. S. (2018). Reduced order modeling for nonlinear structural analysis using Gaussian process regression. Computer methods in applied mechanics and engineering, 341, 807-826. https://doi.org/10.1016/j.cma.2018.07.017

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Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (gebouwnr. 11), kamer 3003
Hallenweg 19
7522NH  Enschede

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Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling  3003
Postbus 217
7500 AE Enschede

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