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Ye, D.
, & Guo, M. (2023).
Bayesian approach to Gaussian process regression with uncertain inputs. ArXiv.org.
Xie, X., Wang, W., Wu, H.
, & Guo, M. (2023).
Data-driven analysis of parametrized acoustic systems in the frequency domain.
Applied mathematical modelling,
124, 791-805.
https://doi.org/10.1016/j.apm.2023.08.018
Cicci, L., Fresca, S.
, Guo, M., Manzoni, A., & Zunino, P. (2023).
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression.
Computers and Mathematics with Applications,
149, 1-23.
https://doi.org/10.1016/j.camwa.2023.08.016
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, Article 115811.
https://doi.org/10.1016/j.cma.2022.115811
Botteghi, N.
, Guo, M.
, & Brune, C. (2022).
Deep kernel learning of dynamical models from high-dimensional noisy data.
Scientific reports,
12, 21530. Article 21530.
https://doi.org/10.1038/s41598-022-25362-4
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, Article 115336.
https://doi.org/10.1016/j.cma.2022.115336
Guo, M., & Haghighat, E. (2022).
Energy-Based Error Bound of Physics-Informed Neural Network Solutions in Elasticity.
Journal of Engineering Mechanics,
148(8), Article 04022038.
https://doi.org/10.1061/(ASCE)EM.1943-7889.0002121
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, Article 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. (2021).
Bayesian operator inference for the reduced order modeling of time-dependent problems. Abstract from 15th Biannual Congress of SIMAI 2021, Parma, Italy.
Guo, M., Hesthaven, J. S., Kast, M., McQuarrie, S. A., & Willcox, K. E. (2021).
Bayesian methods for non-intrusive reduced order modeling. Abstract from Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering and Technology, MMLDT-CSET 2021, San Diego, California, United States.
Guo, M., & Haghighat, E. (2021).
Bounding discretization errors of physics-informed neural network solutions in elasticity. Abstract from 16th U.S. National Congress on Computational Mechanics 2021, Virtual Event, 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
Guo, M., Manzoni, A., Amendt, M., Conti, P., & Hesthaven, J. S. (2021).
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities. ArXiv.org.
https://arxiv.org/abs/2102.13403
Guo, M., & Haghighat, E. (2020).
Energy-based error bound of physics-informed neural network solutions in elasticity. ArXiv.org.
https://arxiv.org/abs/2010.09088
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, Article 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
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Vakken Collegejaar 2023/2024
Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.
Vakken Collegejaar 2022/2023
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Bezoekadres
Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
(gebouwnr. 11), kamer 2051
Hallenweg 19
7522NH Enschede
Postadres
Universiteit Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
2051
Postbus 217
7500 AE Enschede