| Swelling kinetics of polymer gels: comparison of linear and nonlinear theories N Bouklas, R Huang Soft Matter 8 (31), 8194-8203, 2012 | 200 | 2012 |
| An adhesive hydrogel with “Load‐Sharing” effect as tissue bandages for drug and cell delivery J Chen, D Wang, LH Wang, W Liu, A Chiu, K Shariati, Q Liu, X Wang, ... Advanced Materials 32 (43), 2001628, 2020 | 144 | 2020 |
| A nonlinear, transient finite element method for coupled solvent diffusion and large deformation of hydrogels N Bouklas, CM Landis, R Huang Journal of the Mechanics and Physics of Solids 79, 21-43, 2015 | 136 | 2015 |
| Swell-induced surface instability of hydrogel layers with material properties varying in thickness direction Z Wu, N Bouklas, R Huang International Journal of Solids and Structures 50 (3-4), 578-587, 2013 | 100 | 2013 |
| The mixed deep energy method for resolving concentration features in finite strain hyperelasticity JN Fuhg, N Bouklas Journal of Computational Physics 451, 110839, 2022 | 81 | 2022 |
| Engineering transferrable microvascular meshes for subcutaneous islet transplantation W Song, A Chiu, LH Wang, RE Schwartz, B Li, N Bouklas, DT Bowers, ... Nature communications 10 (1), 4602, 2019 | 78 | 2019 |
| On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling JN Fuhg, N Bouklas Computer Methods in Applied Mechanics and Engineering 394, 114915, 2022 | 73 | 2022 |
| A framework for data-driven solution and parameter estimation of pdes using conditional generative adversarial networks T Kadeethum, D O’Malley, JN Fuhg, Y Choi, J Lee, HS Viswanathan, ... Nature Computational Science 1 (12), 819-829, 2021 | 72 | 2021 |
| Effect of Solvent Diffusion on Crack-Tip Fields and Driving Force for Fracture of Hydrogels N Bouklas, CM Landis, R Huang Journal of Applied Mechanics 82 (8), 081007 1-16, 2015 | 72 | 2015 |
| Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks JN Fuhg, M Marino, N Bouklas Computer Methods in Applied Mechanics and Engineering 388, 114217, 2022 | 58 | 2022 |
| Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques T Kadeethum, F Ballarin, Y Choi, D O’Malley, H Yoon, N Bouklas Advances in Water Resources 160, 104098, 2022 | 57 | 2022 |
| A variational phase-field model for brittle fracture in polydisperse elastomer networks B Li, N Bouklas International Journal of Solids and Structures 182, 193-204, 2020 | 56 | 2020 |
| Model-data-driven constitutive responses: Application to a multiscale computational framework JN Fuhg, C Böhm, N Bouklas, A Fau, P Wriggers, M Marino International Journal of Engineering Science 167, 103522, 2021 | 55 | 2021 |
| Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations JN Fuhg, L van Wees, M Obstalecki, P Shade, N Bouklas, M Kasemer Materialia 23, 101446, 2022 | 32 | 2022 |
| An adhesive and resilient hydrogel for the sealing and treatment of gastric perforation J Chen, JS Caserto, I Ang, K Shariati, J Webb, B Wang, X Wang, ... Bioactive Materials 14, 52-60, 2022 | 31 | 2022 |
| Rate-dependent damage mechanics of polymer networks with reversible bonds SC Lamont, J Mulderrig, N Bouklas, FJ Vernerey Macromolecules 54 (23), 10801-10813, 2021 | 31 | 2021 |
| Learning hyperelastic anisotropy from data via a tensor basis neural network JN Fuhg, N Bouklas, RE Jones Journal of the Mechanics and Physics of Solids 168, 105022, 2022 | 30 | 2022 |
| Modular machine learning-based elastoplasticity: Generalization in the context of limited data JN Fuhg, CM Hamel, K Johnson, R Jones, N Bouklas Computer Methods in Applied Mechanics and Engineering 407, 115930, 2023 | 27 | 2023 |
| Engineered extracellular matrices with integrated wireless microactuators to study mechanobiology FE Uslu, CD Davidson, E Mailand, N Bouklas, BM Baker, MS Sakar Advanced Materials 33 (40), 2102641, 2021 | 26 | 2021 |
| Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties T Kadeethum, D O’Malley, Y Choi, HS Viswanathan, N Bouklas, H Yoon Computers & Geosciences 167, 105212, 2022 | 25 | 2022 |
Jan Niklas FuhgUT Austin在 utexas.edu 的电子邮件经过验证
