| 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 | 769 | 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 | 189 | 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 | 122 | 2021 |
| Physics-informed neural networks for nonhomogeneous material identification in elasticity imaging E Zhang, M Yin, GE Karniadakis arXiv preprint arXiv:2009.04525, 2020 | 94 | 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 | 67 | 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 | 50 | 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 | 39 | 2019 |
| 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 | 35 | 2022 |
| A reduced-order aerodynamic model with high generalization capability based on neural network Y Minglang, K Jiaqing, Z Weiwei Acta Aerodynamica Sinica 35 (2), 205-213, 2017 | 10 | 2017 |
| 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 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 | 8 | 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 | 8 | 2021 |
| 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 | 6 | |
| Dimon: Learning solution operators of partial differential equations on a diffeomorphic family of domains M Yin, N Charon, R Brody, L Lu, N Trayanova, M Maggioni arXiv preprint arXiv:2402.07250, 2024 | 3 | 2024 |
| 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 |
| 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 |
| 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 preprint arXiv:2403.08685, 2024 | | 2024 |
| PO-01-212 A NOVEL DEEP LEARNING MODEL FOR PATIENT-SPECIFIC COMPUTATIONAL MODELING OF CARDIAC ELECTROPHYSIOLOGY M Yin, L Lu, M Maggioni, NA Trayanova Heart Rhythm 20 (5), S163, 2023 | | 2023 |
| CE-452775-2 MARS-HCM: MULTI-MODAL DEEP LEARNING METHOD FOR VENTRICULAR ARRHYTHMIA (VA) RISK STRATIFICATION IN HYPERTROPHIC CARDIOMYOPATHY (HCM) PATIENTS C Lai, DM Popescu, M Yin, D Lu, JK Shade, M Engels, E Binka, J Chrispin, ... Heart Rhythm 20 (5), S46-S47, 2023 | | 2023 |
| Hybrid Computational-Machine Learning Models with Uncertainty Quantification for Aortic Dissection M Yin Brown University, 2022 | | 2022 |
