(Note: "#" indicates the students or postdocs under my supervision)
2023
[24] Taneja, K., He, X., He, Q., & Chen, J. S. (2023). A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems. Computational Mechanics. [URL] [Preprint]
[23] He, Q., Perego, M., Howard, A. A., Karniadakis, G. E., & Stinis, P. (2023) A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling. Journal of Computational Physics. [URL] [Preprint]
[22] Zong, Y., He, Q., & Tartakovsky, A. M. (2023). Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions. Computer Methods in Applied Mechanics and Engineering, 414, 116125. [URL]
[21] Du, H.#, Zhao, Z. Cheng, H., Yan, J., He, Q. (2023) Modeling density-driven flow in porous media by physics-informed neural networks for CO2 sequestration. Computers and Geotechnics. [URL]
[20] Feng, D., Tan, Z., He, Q. (2023) Physics-informed neural networks of the Saint-Venant equations for flood modeling. Water Resources Research. [Preprint] [URL]
Preprint & Manuscript Under Review
- Du, H.#, He, Q. (2023). Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics, under review. [Preprint]
2022
[19] Taneja, K., He, X., He, Q., Zhao, X., Lina, Y., Loh, K., Chen, J.S. (2022) A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculo-Skeletal Systems, Journal of Biomechanical Engineering. [URL]
[18] He, Q., Fu, Y., Stinis, P., Tartakovsky, A. (2022) Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery, Journal of Power Sources, 542, 231807. [Preprint] [URL]
[17] He, Q., Stinis, P. Tartakovsky, A. (2022). Physics-constrained deep neural network method for estimating parameters in a redox flow battery. Journal of Power Sources, 528, 231147. [Preprint] [URL]
2021
[16] He, X., He, Q., Chen, J. S. (2021). Deep autoencoders for physics-constrained data-driven nonlinear materials modeling. Computer Methods in Applied Mechanics and Engineering, 385, 114034. [URL]
[15] He, Q., Tartakovsky, A. (2021). Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations. Water Resources Research, 57(7), e2020WR029479. [Preprint] [URL] [Codes]
[14] Kaneko, S., Wei, H., He, Q., Chen, J. S., Yoshimura, S. (2021). A hyper-reduced meshfree method for fast prediction of thermal fatigue behaviors of electronic packages. Journal of the Mechanics and Physics of Solids, 151, 104385. [URL]
2020
[13] He, X., He, Q., Chen, J. S., Shinha, U., Shinha, S. (2020) Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids. Data-Centric Engineering, 1. [URL]
[12] He, Q., Laurence, D., Lee, C. H., Chen, J. S. (2020). Manifold learning-based data-driven modeling for soft biological tissues. Journal of Biomechanics, 117, 110124. [URL]
[11] Tartakovsky, A., Barajas-Solano, D., He, Q. (2020). Physics-informed machine learning with conditional Karhunen–Loève expansion. Journal of Computational Physics, 426, 109904. [Preprint] [URL]
[10] He, Q., Barajas-Solano, D., Tartakovsky, G., Tartakovsky, A. (2020). Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport. Advances in Water Resources, 141, 103610. [Preprint] [URL] (Top Cited Articles in AWR)
2019
[9] He, Q. & Chen, J. S. (2019). A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database. Computer Methods in Applied Mechanics and Engineering, 363, 112791. [Preprint] [URL]
[8] Zhang, Y., Chen, J. S., He, Q., He, X., Basava, R., Hodgson, J., Sinha, U. & Sinha, S. (2019). Microstructural Analysis of Skeletal Muscle Force Generation During Ageing. International Journal for Numerical Methods in Biomedical Engineering, 36(1), e3295. [URL]
2018 and earlier
[7] He, Q., Chen, J. S., & Marodon, C. (2018). A decomposed subspace reduction for fracture mechanics based on the meshfree integrated singular basis function method. Computational Mechanics, 63(3), 593- 614. [URL]
[6] He, Q., Wei, H., Chen, J. S., Wang, H. P., & Carlson, B. E. (2018). Analysis of hot cracking during lap joint laser welding processes using the melting state-based thermomechanical modeling approach. The International Journal of Advanced Manufacturing Technology, 94(9-12), 4373-4386. [URL]
[5] Wei, H., He, Q., Chen, J. S., Wang, H. P., & Carlson, B. E. (2017). Coupled thermal-mechanical-contact analysis of hot cracking in laser welded lap joints. Journal of Laser Applications, 29(2), 022412. [URL]
[4] He, Q., Kang, Z., & Wang, Y. (2014). A topology optimization method for geometrically nonlinear structures with meshless analysis and independent density field interpolation. Computational Mechanics, 54(3), 629-644. [URL]
[3] Wang, Y., Kang, Z., & He, Q. (2014). Adaptive topology optimization with independent error control for separated displacement and density fields. Computers & Structures, 135, 50-61. [URL]
[2] Wang, Y., Kang, Z., & He, Q. (2013). An adaptive refinement approach for topology optimization based on separated density field description. Computers & Structures, 117, 10-22. [URL]
[1] He, Q., Hu, H., Belouettar, S., Guinta, G., Yu, K., Liu, Y., Biscani, F., Carrera, E. & Potier-Ferry, M. (2011). Multi-scale modelling of sandwich structures using hierarchical kinematics. Composite Structures, 93(9), 2375-2383. [URL]
Other Publications (conference proceedings, open-access articles, etc.)
Wang, D., Bao, J., Zamarripa-Perez, M. A., Paul, B., Chen, Y., Gao, P., ... & Xu, Z. (2023). A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems. Energy Advances, 2(10), 1735-1751.
He, X., He, Q., & Chen, J. S. (2021). Deep Autoencoders for Nonlinear Physics-Constrained Data-Driven Computational Framework with Application to Biological Tissue Modeling. In AAAI Spring Symposium: MLPS. (conference proceedings)
It would be greatly appreciated if you have any comment and critique. Please contact qzhe 'at' umn.edu.
