Physics-informed neural networks (PINNs) for fluid mechanics: A review S Cai, Z Mao, Z Wang, M Yin, GE Karniadakis Acta Mechanica Sinica 37 (12), 1727-1738, 2021 | 1346 | 2021 |
A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials S Goswami, M Yin, Y Yu, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 391, 114587, 2022 | 317 | 2022 |
Non-invasive inference of thrombus material properties with physics-informed neural networks M Yin, X Zheng, JD Humphrey, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 375, 113603, 2021 | 170 | 2021 |
Physics-informed neural networks for nonhomogeneous material identification in elasticity imaging E Zhang, M Yin, GE Karniadakis AAAI Fall 2020 Symposium on Physics-Guided AI to Accelerate Scientific Discovery, 2020 | 120 | 2020 |
Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems M Yin, E Zhang, Y Yu, GE Karniadakis Computer methods in applied mechanics and engineering 402, 115027, 2022 | 96 | 2022 |
Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator–regression neural network M Yin, E Ban, BV Rego, E Zhang, C Cavinato, JD Humphrey, ... Journal of the Royal Society Interface 19 (187), 20210670, 2022 | 53 | 2022 |
Novel Wiener models with a time-delayed nonlinear block and their identification J Kou, W Zhang, M Yin Nonlinear Dynamics 85, 2389-2404, 2016 | 51 | 2016 |
One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization M Yin, A Yazdani, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 353, 66-85, 2019 | 48 | 2019 |
A scalable framework for learning the geometry-dependent solution operators of partial differential equations M Yin, N Charon, R Brody, L Lu, N Trayanova, M Maggioni Nature Computational Science 4 (12), 928-940, 2024 | 17* | 2024 |
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes M Yin, Z Zou, E Zhang, C Cavinato, JD Humphrey, GE Karniadakis Journal of the Mechanics and Physics of Solids, 2023 | 11 | 2023 |
Multiscale parareal algorithm for long-time mesoscopic simulations of microvascular blood flow in zebrafish AL Blumers, M Yin, H Nakajima, Y Hasegawa, Z Li, GE Karniadakis Computational Mechanics 68 (5), 1131-1152, 2021 | 10 | 2021 |
一种高泛化能力的神经网络气动力降阶模型 尹明朗, 寇家庆, 张伟伟 空气动力学学报, 2017 | 9 | 2017 |
A reduced-order aerodynamic model with high generalization capability based on neural network Y Minglang, K Jiaqing, Z Weiwei 空气动力学学报 35 (2), 205-213, 2017 | 9 | 2017 |
Physics-informed neural networks (PINNs) for fluid mechanics: A review. arXiv 2021 S Cai, Z Mao, Z Wang, M Yin, GE Karniadakis arXiv preprint arXiv:2105.09506, 0 | 9 | |
Heat transfer in nanofluid boundary layer near adiabatic wall D Hopper, D Jaganathan, JL Orr, J Shi, F Simeski, M Yin, JTC Liu Journal of Nanofluids 7 (6), 1297-1302, 2018 | 2 | 2018 |
Physics-informed neural networks (pinns) for fluid mechanics: A review, 2021 S Cai, Z Mao, Z Wang, M Yin, GE Karniadakis URL https://arxiv. org/abs/2105.09506, 0 | 2 | |
Elastic shape analysis computations for clustering left atrial appendage geometries of atrial fibrillation patients Z Ahmad, M Yin, Y Sukurdeep, N Rotenberg, E Kholmovski, ... ArXiv, 2024 | 1 | 2024 |
Nanofluid thermal boundary layer JTC Liu, D Hopper, D Jaganathan, JL Orr, J Shi, F Simeski, M Yin Book of Abstracts, 273, 2017 | 1 | 2017 |
Convolutional Deep Operator Networks for Learning Nonlinear Focused Ultrasound Wave Propagation in Heterogeneous Spinal Cord Anatomy A Kumar, X Zhi, Z Ahmad, M Yin, A Manbachi arXiv preprint arXiv:2412.16118, 2024 | | 2024 |
Peripheral arterial pathology and osteoarthritis of the knee: US examination of arterial wall stiffness, thickness, and flow characteristics J Olansen, M Yin, J Molino, T Carruthers, D Jenkins, G Karniadakis, ... Osteoarthritis and Cartilage Open 6 (4), 100537, 2024 | | 2024 |