Research

Research Interest:

Data Science, Scientific Machine Learning

Multi-Fidelity Modeling

Uncertainty Quantification, Sensitivity Analysis

Reduced Basis Method, Model Reduction

High Order Numerical Methods (Discontinuous Galerkin methods, WENO)

High Performance Computing, GPGPU computing

Publication:

[29] Bertaglia, G., Liu, L., Pareschi, L. and Zhu, X., Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties. Networks and Heterogeneous Media, 17(3), p.401,2022

[28] Park, J.S.R. and Zhu, X., Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations, Accepted, Journal of Computational Physics, arXiv preprint arXiv:2202.09712, 2022

[27] Liu, L., Pareschi, L. and Zhu, X., A bi-fidelity stochastic collocation method for transport equations with diffusive scaling and multi-dimensional random inputs. Journal of computational physics, 462, 1112522022， 2022, [link]

[26] Zhang, Y., Zhu, X. and Gao, J., Hidden physics model for parameter estimation of elastic wave equations. Computer Methods in Applied Mechanics and Engineering, 381, p.113814, 2021

[25] Lu, C., Zhu, X. Bifidelity Data-Assisted Neural Networks in Nonintrusive Reduced-Order Modeling. J Sci Comput 87, 8 (2021), [link]

[24] Zhang, Y., Yu, S., Zhu, X., Ning, X., Liu, W., Wang, C., Liu, X., Zhao, D., Zheng, Y. and Bao, J., Explainable liver tumor delineation in surgical specimens using hyperspectral imaging and deep learning. Biomedical optics express, 12(7), pp.4510-4529, 2021

[23] J. Zhang, X. Zhu, J. Bao, Solver-informed neural networks for spectrum reconstruction of colloidal quantum dot spectrometers, 28 (22), 33656-33672, Optical Express, 2020,[link]

[22] A.Pensoneault, X. Yang, X. Zhu, Nonnegativity-Enforced Gaussian Process Regression, Theoretical and Applied Mechanics Letters, accepted, 2020,[link]

[21] Y. Zhang, X.Zhu, J. Gao, Parameters Estimation Of Acoustic Wave Equations Using Hidden Physics Models, IEEE Transactions on Geoscience and Remote Sensing, 2020, [link]

[20] X.Yang, X.Zhu, J.Li, When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method, SIAM Scientific Computing, 2020 ,[link]

[19] H.Gao, X.Zhu, J.Wang, A Bi-fidelity Surrogate Modeling Approach for Uncertainty Propagation in Three-Dimensional Hemodynamic Simulations, Computer Methods in Applied Mechanics and Engineering, accepted, 2020, [link]

[18] S. Li, X. Zhu, Y. Zhang, Y. Zheng, J. Bao, Multi-stage Spatial Feature Integration for Multispectral Image Classification, IEEE ASID, 2019, [link]

[J17] L.Liu, X. Zhu, A bi-fidelity method for the multiscale Boltzmann equation with random parameters, Journal of Computational Physics, 2019, [link]

[J16] S. Li, X.Zhu, Y.Liu, J. Bao, Adaptive Spatial-Spectral Feature Learning for Hyperspectral Image Classification, 7(1), 61534, IEEE Access, 2019, [link]

[J15] S. Li, X.Zhu, J. Bao, Hierarchical Multi-scale Convolutional Neural Networks for Hyperspectral Image Classification, 19 (7), 1714, Sensors, 2019, [link]

[J14] M.Cheng; A. Narayan; Y. Qin; P.Wang; X. Zhong; X. Zhu , An Efficient Solver for Cumulative Density Function-based Solutions of Uncertain Kinematic Wave Models, Journal of Computational Physics, 382, 138-151, 2019, [link]

[J13] R Munipalli, J Hamilton, X Zhu, Multifidelity Approach to Parameter Dependent Modeling of Combustion Instability, Joint Propulsion Conference, AIAA Propulsion and Energy Forum, 2018, [link]

[J12]X. Zhu, E.M. Linebarger, D. Xiu, Multi-fidelity stochastic collocation of computation of statistical moments, Journal of Computational Physics, 2017, [link]

[J11] X. Zhu and D. Xiu, A Multi-Fidelity Collocation Method for Time-Dependent Parameterized Problems", 19th AIAA Non-Deterministic Approaches Conference, AIAA SciTech Forum, (AIAA 2017-1094), 2017,[link]

[J10] S. Jin, D. Xiu, X. Zhu, A well-balanced Stochastic Galerkin Method for scalar hyperbolic balance laws with Random Inputs, Journal of Scientific Computing, 67, 1198-1218 , 2016, [link]

[J9] S. Jin, D. Xiu, X. Zhu, Asymptotic preserving methods for hyperbolic and transport equations with random inputs and diffusive scalings, Journal of Computational Physics, 285, 265-279, 2015, [link]

[J8] J.S. Hesthaven, S. Zhang, and X. Zhu, Reduced basis method for high order multiscale finite element method,SIAM Multiscale Modeling and Simulation, 13(1), 316-337, 2015, [link]

[J7] R. Munipalli, Z. Liu, X. Zhu, S Menon, J.S. Hesthaven, Applications of Model Reduction Techniques in Aerospace Combustors, preprint, 2014, [link]

[J6] R. Munipalli, Z. Liu, X. Zhu, S Menon, J.S. Hesthaven, Model Reduction Opportunities in Detailed Simulations of Combustion Dynamics, AIAA paper 2014-0820, 52nd AIAA Aerospace Sciences Meeting, 2014, [link]

[J5] X. Zhu, A.Narayan, and D.Xiu, Computational aspects of stochastic collocation with multi-fidelity models, SIAM/ASA J. Uncertainty Quantification, 2(1), 444–463, 2014, [link]

[J4] J.S. Hesthaven, S. Zhang, and X. Zhu, High-order multiscale finite element methods for elliptic problems, SIAM Multiscale Modeling and Simulation, 12(2), 650–666, 2014, [link]

[J3] F. Chen, J.S. Hesthaven and X. Zhu, On the use of reduced basis methods to accelerate and stabilize the Parareal method, Chapter 7, Reduced Order Methods for Modeling and Computational Reduction (MS&A), Springer, 2013, [link]

[J2] K. Shahbazi, J.S. Hesthaven and X. Zhu, Multi-dimensional hybrid Fourier continuation-WENO solver for conservation laws, Journal of Computational Physics, 253, 209–225, 2013, [link]

[J1] Y. Chen, J.S. Hesthaven, Y. Maday, J. Rodriguez and X. Zhu, Certified reduced methods for electromagnetic scattering and radar cross section prediction, Comput. Methods Appl. Mech. Engin., 233, 92-108, 2012,[link]

[T3] Xueyu Zhu, Reduced Basis Methods and Their applications, PhD thesis, 2013

[T2] Xueyu Zhu, Multiscale Simulation of Flow past Nanotubes, master thesis, 2007,[link]

[T1] Xueyu Zhu, Brownian dynamics simulation of polymer behaviors in microfluidic systems, master thesis, 2005 [link]

Conference and Workshop:

11. SIAM CSE, Atlanta, Georgia, Feb, 2017

10. AIAA SciTech, Dallas, Texas, Jan, 2017

9. SIAM AN, Boston, Massachusetts, July, 2016

8. SIAM UQ, Lausanne, Switzerland, April, 2016

7. TMS 2016, Nashville, Tennessee, Feb, 2016

6. The International Congress on Industrial and Applied Mathematics (ICIAM), Aug, 2015

5. SIAM Applications of Dynamic Systems, Snowbird, Utah, May, 2015

4. SIAM CSE, Salt Lake City, Utah, March, 2015

3. A Stochastic Collocation Approach for Multi-Fidelity Model Classes, SIAM UQ, Savannah, Georgia, March, 2014

2. Towards a Blackbox Approach for Model Reduction via EIM, SIAM CSE, Boston, Massachusetts, 2013

1. GPU accelerated High order Hybrid methods for shock problems, Applied Math Days, RPI, Troy, 2012

Invited Seminars:

5. AMCS Seminar, University of Iowa, Iowa City, Feb, 2017

4. Department of Mathematics Colloquium, Purdue University, West Lafayette, Sep, 2016

3. Math Colloquium, University of Iowa, Iowa City, Dec, 2015

2. ACMS Colloquium, University of Notre Dame, South Bend, Nov, 2015

1. Applied math and PDE seminar, MSU, East Lansing, 2013

Referee for journals/proceedings:

SIAM/ASA Journal on Uncertainty Quantification

International Journal of Uncertainty Quantification

Computer Methods in Applied Mechanics and Engineering

SIAM Journal on Scientific Computing

Journal of Scientific Computing

Journal of Computational Physics

Advances in Computational Mathematics

International Journal of Computer Mathematics

Computers and Mathematics with Applications

The International Conference on Computational Science (ICCS 2014)