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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
Botteghi, N.
, Guo, M.
, & Brune, C. (2022).
Deep kernel learning of dynamical models from high-dimensional noisy data.
Scientific reports,
12, 21530. [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, [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), [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, [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
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, [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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Vakken Collegejaar 2022/2023
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 2021/2022
Contactgegevens
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