Research Expertise

Publications

  • D. Liu, F. G. Atienza, and M. Guo. An adjoint method for training data-driven reduced-order models. arXiv:2601.07579, 2026.
  • S. A. McQuarrie, M. Guo, and A. Chaudhuri. Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference. arXiv:2601.00038, 2026.
  • D. Ye, W. Yan, C. Brune, and M. Guo. PDE-constrained Gaussian process surrogate modeling with uncertain data locations. Advanced Modeling and Simulation in Engineering Sciences, 12:33, 2025. DOI: 10.1186/s40323-025-00308-3
  • P. Conti, M. Guo, A. Frangi, and A. Manzoni. Progressive multi-fidelity learning for physical system predictions. arXiv:2510.13762, 2025.
  • W. Yan, C. Brune, and M. Guo. Physics-based deep kernel learning for parameter estimation in high-dimensional PDEs. arXiv:2509.14054, 2025.
  • T. J. Heeringa, C. Brune, and M. Guo. Sparsifying dimensionality reduction of PDE solution data with Bregman learning. SIAM Journal on Scientific Computing, 47(5):C1033-C1058, 2025. DOI: 10.1137/24M1684566
  • M. Fransen, A. Fürst, D. Tunuguntla, D. N. Wilke, B. Alkin, D. Barreto, J. Brandstetter, M. A. Cabrera, X. Fan, M. Guo, B. Kieskamp, K. Kumar, J. Morrissey, J. Nuttall, J. Ooi, L Orozco, S. Papanicolopulos, T. Qu, D. Schott, T. Shuku, W. Sun, T. Weinhart, D. Ye, and H. Cheng. Towards scientific machine learning for granular material simulations: challenges and opportunities. Archives of Computational Methods in Engineering, 2025. DOI: 10.1007/s11831-025-10322-8
  • S. A. McQuarrie, A. Chaudhuri, K. E. Willcox, and M. Guo. Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems. Physica D: Nonlinear Phenomena, 475:134572, 2025. DOI: 10.1016/j.physd.2025.134572
  • W. Yan, C. Brune, and M. Guo. PDE-DKL: PDE-constrained deep kernel learning in high dimensionality. arXiv:2501.18258, 2025.
  • N. Botteghi, S. Fresca, M. Guo, and A. Manzoni. HypeRL: Parameter-informed reinforcement learning for parametric PDEs. arXiv:2501.04538, 2025.
  • V. Sella, T. O'Leary-Roseberry, X. Du. M. Guo, J . R. R. A. Martins, O. Ghattas, K. Willcox, and A. Chaudhuri. Improving neural network efficiency with multifidelity and dimensionality reduction techniques. AIAA SciTech Forum, 2025. DOI: 10.2514/6.2025-2807​
  • D. Ye and M. Guo. Gaussian process learning of nonlinear dynamics. Communications in Nonlinear Science and Numerical Simulation, 138:108184, 2024. DOI: 10.1016/j.cnsns.2024.108184
  • N. Botteghi, P. Motta, A. Manzoni, P. Zunino, and M. Guo. Recurrent deep kernel learning of dynamical systems. I. V. Gosea, K. Cherifi (eds.), Physics-Based and Data-Driven Modeling for Digital Twins, ICIAM 2023 Book Series, Springer, 2026. (To appear in February, 2026) arXiv:2405.19785
  • P. Conti, M. Guo, A. Manzoni, A. Frangi, S. L. Brunton, and J. N. Kutz. Multi-fidelity reduced-order surrogate modeling. Proceedings of the Royal Society A, 48:20230655, 2024. DOI: 10.1098/rspa.2023.0655
  • X. Xie, W. Wang, H. Wu, and M. Guo. Data-driven analysis of parametrized acoustic systems in the frequency domain. Applied Mathematical Modelling, 124:791-805, 2023. DOI: 10.1016/j.apm.2023.08.018
  • L. Cicci, S. Fresca, M. Guo, A. Manzoni, and P. Zunino. Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. Computers & Mathematics with Applications, 149:1-23, 2023. DOI: 10.1016/j.camwa.2023.08.016
  • P. Conti, M. Guo, A. Manzoni, and J. S. Hesthaven. Multi-fidelity surrogate modeling using long short-term memory networks. Computer Methods in Applied Mechanics and Engineering, 404:115811, 2023. DOI: 10.1016/j.cma.2022.115811​
  • N. Botteghi, M. Guo, and C. Brune. Deep kernel learning of dynamical models from high-dimensional noisy data. Scientific Reports, 12:21530, 2022. DOI: 10.1038/s41598-022-25362-4​
  • M. Guo, S. A. McQuarrie, and K. E. Willcox. Bayesian operator inference for data-driven reduced-order modeling. Computer Methods in Applied Mechanics and Engineering, 402:115336, 2022. DOI: 10.1016/j.cma.2022.115336
  • M. Guo and E. Haghighat. Energy-based error bound of physics-informed neural network solutions in elasticity. Journal of Engineering Mechanics, 148(8):04022038, 2022. DOI: 10.1061/(ASCE)EM.1943-7889.0002121; arXiv: 2010.09088
  • M. Guo, A. Manzoni, M. Amendt, P. Conti, and J. S. Hesthaven. Multifidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities. Computer Methods in Applied Mechanics and Engineering, 389:114378, 2022. DOI: 10.1016/j.cma.2021.114378​
  • C. Bigoni, M. Guo, and J. S. Hesthaven. Predictive monitoring of large-scale engineering assets using machine learning techniques and reduced order modeling. A. Cury et al. (eds.), Structural Health Monitoring Based on Data Science Techniques, Structural Integrity 21, Springer, 2021. DOI: 10.1007/987-3-030-81716-9_9 
  • M. Guo and C. Brune. Uncertainty quantification for physics-informed deep learning. W. H. A. Schilders (ed.), Mathematics: Key Enabling Technology for Scientific Machine Learning, Platform Wiskunder Nederland, 2021. link
  • M. Guo. A brief note on understanding neural networks as Gaussian processes. arXiv:2107.11892, 2021.
  • M. Kast, M. Guo, and J. S. Hesthaven. A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems. Computer Methods in Applied Mechanics and Engineering, 364:112947, 2020. DOI: 10.1016/j.cma.2020.112947
  • J. Yu, C. Yan, and M. Guo. 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, 2019. DOI: 10.1177/0954410019890721
  • Z. Zhang, M. Guo, and J. S. Hesthaven. Model order reduction for large-scale structures with local nonlinearities. Computer Methods in Applied Mechanics and Engineering, 353:491-515, 2019. DOI: 10.1016/j.cma.2019.04.042
  • M. Guo and J. S. Hesthaven. Data-driven reduced order modeling for time-dependent problems. Computer Methods in Applied Mechanics and Engineering, 345:75–99, 2019. DOI: 10.1016/j.cma.2018.10.029
  • M. Guo and J. S. Hesthaven. Reduced order modeling for nonlinear structural analysis using Gaussian process regression. Computer Methods in Applied Mechanics and Engineering, 341:807-826, 2018. DOI: 10.1016/j.cma.2018.07.017
  • M. Guo and H. Zhong. Strict upper and lower bounds for quantities of interest in static response sensitivity analysis. Applied Mathematical Modelling, 49:17-34, 2017. DOI: 10.1016/j.apm.2017.04.029
  • M. Guo and H. Zhong. Equivalence of two strictly bounding approaches for goal-oriented error estimation. Journal of Tsinghua University, Science and Technology, 57(4):362–368, 2017. (in Chinese)
  • M. Guo and H. Zhong. Constitutive-relation-error-based a posteriori error bounds for a class of elliptic variational inequalities. Applied Mathematics Letters, 71:14-23, 2017. DOI: 10.1016/j.aml.2017.03.007​
  • M. Guo, W. Han, and H. Zhong. Legendre-Fenchel duality and a generalized constitutive relation error. arXiv:1611.05589, 2016.
  • ​M. Guo and H. Zhong. Weak form quadrature solution of 2mth-order Fredholm integro-differential equations. International Journal of Computer Mathematics, 93(10):1650-1664, 2016. DOI: 10.1080/00207160.2015.1070839
  • M. Guo, H. Zhong, and K. You. A second-order perturbation method for fuzzy eigenvalue problems. Engineering Computations, 33(2):306-327, 2016. ​DOI: 10.1108/EC-01-2015-0024  
  • M. Guo and H. Zhong. Identification of imperfections in thin plates based on the modified potential energy principle. Mechanics Research Communications, 72:16-23, 2016.  DOI: 10.1016/j.mechrescom.2016.01.001
  • M. Guo and H. Zhong. Goal-oriented error estimation for beams on elastic foundation with double shear effect. Applied Mathematical Modelling, 39(16):5047-5057, 2015. DOI: 10.1016/j.apm.2015.04.021
  • L. Wang, M. Guo, and H. Zhong. Strict upper and lower bounds of quantities for beams on elastic foundation by dual analysis. Engineering Computations, 32(6):1619-1642, 2015. DOI: 10.1108/EC-04-2014-0094 
  • M. Guo. Mathematical equivalence of the integration method and the unit load method. Mechanics in Engineering, 35(4):70-72, 2013. (in Chinese)​
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