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Publications

2022

  1. Rossy, T., Distler, T., Pezoldt, J., Kim, J., Tala, L., Bouklas, N., Deplancke, B. and Persat, A., 2022. Pseudomonas aeruginosa contracts mucus to rapidly form biofilms in tissue-engineered human airways. BioRxiv. doi: https://doi.org/10.1101/2022.05.26.493615
  2. Kadeethum, T., Ballarin, F., O’Malley, D., Choi, Y., Bouklas, N. and Yoon, H., (2022). Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. arXiv preprint arXiv:2202.05460.
  3. Mulderrig, J., Talamini, B. and Bouklas, N., (2022). A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching. arXiv preprint arXiv:2208.07352.
  4. Fuhg, J.N., Fau, A., Bouklas, N. and Marino, M., (2022). Elasto-plasticity with convex model-data-driven yield functions. Hal preprint, hal-03619186
  5. Fuhg, J.N., Karmarkar, A., Kadeethum, T., Yoon, H. and Bouklas, N., 2022. Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data. arXiv preprint arXiv:2206.04675.
  6. Fontenele, F.F. and Bouklas, N., (2022). Understanding the inelastic response of collagen fibrils: A viscoelastic-plastic constitutive model. Acta Biomaterialia.
  7. Darkes-Burkey, C., Liu, X., Slyker, L., Mulderrig, J., Pan, W., Giannelis, E.P., Shepherd, R.F., Bonassar, L.J. and Bouklas, N., (2022). Simple synthesis of soft, tough, and cytocompatible biohybrid composites. Proceedings of the National Academy of Sciences, 119(28), p.e2116675119.
  8. Ang, I., Bouklas, N. and Li, B., (2022). Stabilized formulation for phase‐field fracture in nearly incompressible hyperelasticity. International Journal for Numerical Methods in Engineering.
  9. Fuhg, J.N., van Wees, L., Obstalecki, M., Shade, P., Bouklas, N. and Kasemer, M., (2022). Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations. Materialia, 23, p.101446.
  10. Fuhg, J.N., Bouklas, N. and Jones, R.E., 2022. Learning hyperelastic anisotropy from data via a tensor basis neural network. Journal of Mechanics and Physics of Solids
  11. Ferreira, C.A., Kadeethum, T., Bouklas, N. and Nick, H.M., (2022). A framework for upscaling and modelling fluid flow for discrete fractures using conditional generative adversarial networks. Advances in Water Resources, 166, p.104264.
  12. Kim, B., Middendorf, J.M., Diamantides, N., Cohen, I., Bouklas, N. and Bonassar, L.J., (2022). The role of buckling instabilities in the global and local mechanical response in porous collagen. Accepted: Experimental Mechanics, 1-11.
  13. Fontenele, F.F., Andarawis-Puri, N., Agoras, M. and Bouklas, N., (2022). Fiber plasticity and loss of ellipticity in soft composites under non-monotonic loading. International Journal of Solids and Structures, p.111628.
  14. Fuhg, J.N., Kalogeris, I., Fau, A. and Bouklas, N., (2022). Interval and fuzzy physics-informed neural networks for uncertain fields. Probabilistic Engineering Mechanics, 68, p.103240.
  15. Fuhg, J. N., & Bouklas, N. (2022). On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. Computer Methods in Applied Mechanics and Engineering 394 (2022): 114915
  16. Kadeethum, T., Ballarin, F., Cho, Y., O’Malley, D., Yoon, H. and Bouklas, N., (2022). Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. Advances in Water Resources,104098
  17. Fuhg, J.N. and Bouklas, N. (2022). The mixed deep energy method for resolving concentration features in finite strain hyperelasticity. Journal of Computational Physics, 451, 110839
  18. Fuhg, J.N., Marino, M. and Bouklas, N., (2022). Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks. Computer Methods in Applied Mechanics and Engineering, 388, p.114217.
  19. Mailand, E., Özelçi, E., Kim, J., Rüegg, M., Chaliotis, O., Märki, J., Bouklas, N. and Sakar, M.S., (2022). Tissue engineering with mechanically induced solid‐fluid transitions. Advanced Materials, p.2106149.
  20. Chen, J., Caserto, J.S., Ang, I., Shariati, K., Webb, J., Wang, B., Wang, X., Bouklas, N. and Ma, M., (2022). An adhesive and resilient hydrogel for the sealing and treatment of gastric perforation. Bioactive Materials. 14, 52-60

2021

  1. Kadeethum, T., O’Malley, D., Choi, Y., Viswanathan, H.S.,Bouklas, N., Yoon H., (2021). Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. https://arxiv.org/abs/2111.14984
  2. Lamont, S. C., Mulderrig, J., Bouklas, N., & Vernerey, F. J. (2021). Rate-Dependent Damage Mechanics of Polymer Networks with Reversible Bonds. Macromolecules.
  3. Fuhg, J. N., & Bouklas, N. (2021). On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. arXiv preprint arXiv:2109.11028.
  4. Kadeethum, T., O’Malley, D., Fuhg, J.N., Choi, Y., Lee, J., Viswanathan, H.S. and Bouklas, N., (2021). A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. Nature Computational Science, 1 (12), 819-829
  5. Uslu, F. E., Davidson, C. D., Mailand, E., Bouklas, N., Baker, B. M., & Sakar, M. S. (2021). Engineered Extracellular Matrices with Integrated Wireless Microactuators to Study Mechanobiology. Advanced Materials, p.2102641.
  6. Fuhg, J.N., Böhm, C., Bouklas, N., Fau, A., Wriggers, P. and Marino, M. (2021). Model-data-driven constitutive responses: application to a multiscale computational framework. International Journal of Engineering Science, 167, 103522
  7. Kadeethum, T., Ballarin, F. and Bouklas, N., 2021. Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation. GEM-International Journal on Geomathematics, 12(1), pp.1-45.
  8. Sun, Y., Gorobstov, O., Mu, L., Weinstock, D., Bouck, R., Cha, W., Bouklas, N., Lin, F. and Singer, A., (2021). X-ray Nanoimaging of Crystal Defects in Single Grains of Solid-State Electrolyte Li7–3 x Al x La3Zr2O12. Nano Letters.
  9. Mulderrig, J., Li, B. and Bouklas, N., (2021).Affine and non-affine microsphere models for chain scission in polydisperse elastomer networks. Mechanics of Materials, p.103857.
  10. Kim, J., Mailand, E., Ang, I., Sakar, M.S. and Bouklas, N., (2021). A model for 3D deformation and reconstruction of contractile microtissues. Soft Matter.

2020

  1. Chen, J., Wang, D., Wang, L.H., Liu, W., .., Bouklas, N., Ma, M., 2020. An Adhesive Hydrogel with “Load‐Sharing” Effect as Tissue Bandages for Drug and Cell Delivery. Advanced Materials, p.2001628.
  2. Ang, I., Liu, Z., Kim, J., Hui, C.Y. and Bouklas, N., 2020. Effect of elastocapillarity on the swelling kinetics of hydrogels. Journal of the Mechanics and Physics of Solids, p.104132.
  3. Liu, Z., Bouklas, N. and Hui, C.Y., 2020. Coupled flow and deformation fields due to a line load on a poroelastic half space: effect of surface stress and surface bending. Proceedings of the Royal Society A, 476(2233), p.20190761.
  4. Yu, Y., Bouklas, N., Landis, C.M. and Huang, R., 2020. Poroelastic Effects on the Time and Rate Dependent Fracture of Polymer Gels. Journal of Applied Mechanics, pp.1-25.
  5. Li, B. and Bouklas, N., 2020. A variational phase-field model for brittle fracture in polydisperse elastomer networks. International Journal of Solids and Structures, 182, pp.193-204.
  6. Kim, J., Mailand, E., Ang, I., Sakar, M.S. and Bouklas, N., 2020. A model for 3D deformation and reconstruction of contractile microtissues. Soft Matter.

2019

  1. Song, W., Chiu, A., Wang, L.H., Schwartz, R.E., Li, B., Bouklas, N., Bowers, D.T., An, D., Cheong, S.H., Flanders, J.A., Pardo, Y., Liu, Q., Xi, W., Lee, V.K., Dai, G. and Ma, M., 2019. Engineering transferrable microvascular meshes for subcutaneous islet transplantation. Nature communications, 10(1), pp.1-12.
  2. Mailand, E., Li, B., Eyckmans, J., Bouklas, N. and Sakar, M.S., 2019. Surface and bulk stresses drive morphological changes in fibrous microtissues. Biophysical journal, 117(5), pp.975-986.

2018

  1. Bouklas, N., Sakar, M.S. and Curtin, W.A., 2018. A model for cellular mechanotransduction and contractility at finite strain. ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 98(10), pp.1754-1770.
  2. Yu, Y., Bouklas, N., Landis, C.M. and Huang, R., 2018. A Linear Poroelastic Analysis of Time-Dependent Crack-Tip Fields in Polymer Gels. Journal of Applied Mechanics, 85(11), p.111011.

prior to 2017

  1. Wu, Z., Bouklas, N., Liu, Y. and Huang, R., 2017. Onset of swell-induced surface instability of hydrogel layers with depth-wise graded material properties. Mechanics of Materials, 105, pp.138-147.
  2. Bouklas, N., Landis, C.M. and Huang, R., 2015. A nonlinear, transient finite element method for coupled solvent diffusion and large deformation of hydrogels. Journal of the Mechanics and Physics of Solids, 79, pp.21-43.
  3. Bouklas, N., Landis, C.M. and Huang, R., 2015. Effect of solvent diffusion on crack-tip fields and driving force for fracture of hydrogels. Journal of Applied Mechanics, 82(8), p.081007.
  4. Wu, Z., Bouklas, N. and Huang, R., 2013. Swell-induced surface instability of hydrogel layers with material properties varying in thickness direction. International Journal of Solids and Structures, 50(3-4), pp.578-587.
  5. Bouklas, N. and Huang, R., 2012. Swelling kinetics of polymer gels: comparison of linear and nonlinear theories. Soft Matter, 8(31), pp.8194-8203.