Minglang Yin

Minglang Yin

Postdoc Fellow, Johns Hopkins University

Publications

Journals

  1. Z. Ahmad, M. Yin, Y. Sukurdeep, N. Rotenberg, E. Kholmovski, NA Trayanova, Elastic shape analysis computations for clustering left atrial appendage geometries of atrial fibrillation patients. ArXiv (2024)

  2. M. Yin, N. Charon, R. Brody, L. Lu, N. Trayanova, M. Maggioni, Dimon: Learning solution operators of partial differential equations on a diffeomorphic family of domains. ArXiv (2024)

  3. M. Yin, Z. Zou, E. Zhang, C. Cavinato, J.D. Humphrey, G.E. Karniadakis, “A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes” J MECH PHYS SOLIDS (2023).

  4. M. Yin, E. Zhang, Y. Yu, G.E. Karniadakis, “Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems”, Computer Methods in Applied Mechanics and Engineering, (2022): 115027.

  5. M. Yin, E. Ban, E. Zhang, B. Rego, C. Cavinato, J.D. Humphrey, G.E. Karniadakis, “Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network”, Journal of the Royal Society Interface 19.187 (2022): 20210670.

  6. S. Goswami, M. Yin, Y. Yu, G.E. Karniadakis, “A physics-informed variational DeepONet for predicting the crack path in brittle materials”, Computer Methods in Applied Mechanics and Engineering 391 (2022): 114587.

  7. S. Cai, Z. Mao, Z. Wang, M. Yin, G.E. Karniadakis, “Physics-informed neural networks in fluid mechanics: A review”, Acta Mechanica Sinica (2022): 1-12.

  8. A. Blumers, M. Yin, Y. Hasegawa, Z. Li, and G.E. Karniadakis. “Supervised parallel-in-time algorithm for long-time Lagrangian simulations of stochastic dynamics: Application to blood flow in zebrafish”, Computational Mechanics 68.5 (2021): 1131-1152.

  9. M. Yin, X. Zheng, J.D. Humphrey, G.E. Karniadakis, “Non-invasive inference of thrombus material properties with physics-informed neural networks.” Computer Methods in Applied Mechanics and Engineering 375 (2021): 113603.

  10. E. Zhang, M. Yin, G.E. Karniadakis, “Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging”, AAAI Conference (2020).

  11. M. Yin, A. Yazdani, and G.E. Karniadakis. “One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization.” Computer Methods in Applied Mechanics and Engineering, 353 (2019): 66-85.

  12. D. Hopper, D. Jaganathan, J. Orr, J. Shi, F. Simeski, M. Yin, J.T.C. Liu, “Heat Transfer in Nanofluid Boundary Layer Near Adiabatic Wall.” Journal of Nanofluids 7.6 (2018): 1297-1302.

  13. M. Yin, J. Kou, W. Zhang, “A reduced-order aerodynamic model with high generalization capability based on neural network”, Acta Aerodynamica Sinica 35.02 (2017): 205-213.

  14. J. Kou, W. Zhang, and M. Yin, “Novel Wiener models with a time-delayed nonlinear block and their identification.” Nonlinear Dynamics 85.4 (2016): 2389-2404.