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dr. M. Guo (Mengwu)

Assistant Professor

About Me

Dr. Mengwu Guo is an Assistant Professor in Applied Mathematics at the University of Twente since February 2021. Before joining UT, Mengwu was a Postdoctoral Fellow in the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin. Prior to that, he was a Postdoctoral Researcher in the Institute of Mathematics at Ecole Polytechnique Fédérale de Lausanne (EPFL). Mengwu received his B.Eng. and Ph.D. degrees in Civil Engineering with top honors from Tsinghua University (Beijing, China) in 2013 and 2017, respectively.

Mengwu has been dedicated to the development of high-performance numerical methods towards real-world scientific and engineering applications, and his current research interests include data-driven predictive modeling, model reduction, uncertainty quantification, and physics-informed/scientific machine learning in computational engineering and sciences.

Currently, Mengwu is the UTwente PI in Mathematics of Computational Science of Sectorplan Bèta — a government-level instrument that funds critical research areas of basic sciences in the Netherlands, and coordinator of the Strategic Research Initiative Bridging Numerical Analysis and Machine Learning supported by the Dutch 4TU Applied Mathematics Institute.

Visit Mengwu's website by clicking here.

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Research

Dr. Mengwu Guo's research interests span several areas of computational science and engineering including model order reduction, data-driven modeling, uncertainty quantification, and scientific machine learning. With an interdisciplinary background between engineering sciences and computational mathematics, Mengwu has been dedicated to the development of high-performance numerical methods towards real-world engineering applications.

Publications

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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Visiting Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11), room 2051
Hallenweg 19
7522NH  Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling  2051
P.O. Box 217
7500 AE Enschede
The Netherlands

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University of Twente Drienerlolaan 5
7522 NB Enschede
+31 (0)53 489 9111
info@utwente.nl
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