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Publications

2024

  1. Padmanabha, G.A., Fuhg, J.N., Safta, C., Jones, R.E. and Bouklas, N., 2024. Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. Computer Methods in Applied Mechanics and Engineering, 432, p.117359.
  2. Mousavi, S.M., Ang, I., Mulderrig, J. and Bouklas, N., 2024. Evaluating fracture energy predictions using phase-field and gradient-enhanced damage models for elastomers. Journal of Applied Mechanics, 91(12).
  3. Fuhg, J.N., Padmanabha, G.A., Bouklas, N., Bahmani, B., Sun, W., Vlassis, N.N., Flaschel, M., Carrara, P. and De Lorenzis, L., 2024. A review on data-driven constitutive laws for solids. Archives of Computational Mechanics
  4. Jones, R.E., Hamel, C.M., Bolintineanu, D., Johnson, K., de Macedo, R.B., Fuhg, J., Bouklas, N. and Kramer, S., 2024. Multiscale simulation of spatially correlated microstructure via a latent space representation. International Journal of Solids and Structures
  5. Eghtesad, A., Tan, J., Fuhg, J.N. and Bouklas, N., 2024. NN-EVP: a physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response. International Journal of Plasticity, 181, p.104072.
  6. van Wees, L., Shankar, K., Fuhg, J.N., Bouklas, N., Shade, P., Obstalecki, M. and Kasemer, M., 2024. Establishing the relationship between generalized crystallographic texture and macroscopic yield surfaces using partial input convex neural networks. Materialia, p.102151.
  7. Kim, J., Sakar, M.S. and Bouklas, N., 2024. Modeling the mechanosensitive collective migration of cells on the surface and the interior of morphing soft tissues. Biomechanics and Modeling in Mechanobiology, pp.1-21.
  8. Fuhg, J.N., Bouklas, N. and Jones, R.E., 2023. Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen. Journal of Computing and Information Science in Engineering, pp.1-39.
  9. Upadhyay, K., Fuhg, J.N., Bouklas, N. and Ramesh, K.T., (2023). Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials. arXiv preprint arXiv:2304.13897. (accepted: Computational Mechanics)
  10. Lee, J., Jha, K., Harper, C.E., Zhang, W., Ramsukh, M., Bouklas, N., Dörr, T., Chen, P. and Hernandez, C.J., 2024. Determining the Young’s Modulus of the Bacterial Cell Envelope. ACS Biomaterials Science & Engineering.
  11. Ang, I., Yousafzai, M.S., Yadav, V., Mohler, K., Rinehart, J., Bouklas, N. and Murrell., M., 2024. Elastocapillary effects determine early matrix deformation by glioblastoma cell spheroids. APL bioengineering, 8(2).
  12. Fuhg, J.N., Jones, R.E. and Bouklas, N., 2024. Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. Computer Methods in Applied Mechanics and Engineering, 426, p.116973.
  13. Kim, B., Kelly, T.A.N., Jung, H.J., Beane, O.S., Bhumiratana, S., Bouklas, N., Cohen, I. and Bonassar, L.J., (2024). Microscale strain concentrations in tissue-engineered osteochondral implants are dictated by local compositional thresholds and architecture. Journal of Biomechanics, 162, p.111882.
  14. Kim, J., Ang, I., Ballarin, F., Hui, C.Y. and Bouklas, N., 2024. A finite element implementation of finite deformation surface and bulk poroelasticity. Computational Mechanics, 73(5), pp.1013-1031.
  15. E., Kim, B., Bouklas, N., Bonassar, L.J. and Gaitanaros, S., 2024. 3D in-situ characterization reveals the instability-induced auxetic behavior of collagen scaffolds for tissue engineering. bioRxiv, pp.2024-06.

2023

 

  1. Rossy, T., Distler, T., Pezoldt, J., Kim, J., Tala, L., Bouklas, N., Deplancke, B. and Persat, A., (2023). Pseudomonas aeruginosa contracts mucus to rapidly form biofilms in tissue-engineered human airways. Plos Biology, 21(8).
  2. Fuhg, J.N., Karmarkar, A., Kadeethum, T., Yoon, H. and Bouklas, N., 2023. Deep convolutional Ritz method: parametric PDE surrogates without labeled data. Applied Mathematics and Mechanics, 44(7), pp.1151-1174.
  3. Kadeethum, T., Jakeman, J.D., Choi, Y., Bouklas, N. and Yoon, H., 2023. Epistemic uncertainty-aware Barlow twins reduced order modeling for nonlinear contact problems. IEEE Access.
  4. Kim, B., Bouklas, N., Cohen, I. and Bonassar, L.J., (2023). Instabilities Induced by Mechanical Loading Determine the Viability of Chondrocytes Grown on Porous Scaffolds. Journal of Biomechanics, p.111591.
  5. Kim, J., Mailand, E., Sakar, M.S. and Bouklas, N., A Model for Mechanosensitive Cell Migration in Dynamically Morphing Soft Tissues. (2023) Extreme Mechanics Letters , p.101926.
  6. Fuhg, J.N., Fau, A., Bouklas, N. and Marino, M., (2023). Enhancing phenomenologial yield functions with data: Challenges and opportunities, European Journal of Mechanics-A/Solids, p.104925.
  7. Mulderrig, J., Talamini, B. and Bouklas, N., (2023). A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching. Journal of the Mechanics and Physics of Solids, 174, p.105244.
  8. Fuhg, J. N., Hamel C.M., Johnson K., Jones R., and Bouklas N., (2023). “Modular machine learning-based elastoplasticity: generalization in the context of limited data.” Computer Methods in Applied Mechanics and Engineering 407 (2023): 115930.

2022

  1. Kadeethum, T., O’Malley, D., Choi, Y., Viswanathan, H.S.,Bouklas, N., Yoon H., (2022). Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. Computers & Geosciences, 167, p.105212.
  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. Scientific Reports, 12(1), pp.1-18.
  3. Kadeethum, T., O’Malley, D., Ballarin, F., Ang, I., Fuhg, J.N., Bouklas, N., Silva, V.L., Salinas, P., Heaney, C.E., Pain, C.C., Lee, S., Viswanathan H.S. and Yoon, H., (2022). Enhancing high-fidelity nonlinear solver with reduced order model. Scientific Reports, 12(1), pp.1-15.
  4. Fontenele, F.F. and Bouklas, N., (2022). Understanding the inelastic response of collagen fibrils: A viscoelastic-plastic constitutive model. Acta Biomaterialia. (in-press)
  5. 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.
  6. 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.
  7. 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.
  8. 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 168, 105022.
  9. 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.
  10. 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, 3, 1-11.
  11. 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.
  12. 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.
  13. 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: 114915.
  14. 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
  15. 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.
  16. 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.
  17. 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.

  18. 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. Lamont, S. C., Mulderrig, J., Bouklas, N., & Vernerey, F. J. (2021). Rate-Dependent Damage Mechanics of Polymer Networks with Reversible Bonds. Macromolecules. 54(23), pp.10801-10813.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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. 21(11), pp.4570-4576.
  7. 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.
  8. 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. 17(45), pp.10198-10209.

2020

  1. 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, 145, p.104132
  2. Chen, J., Wang, D., Wang, L.H., Liu, W., Chiu, A., Shariati, K., Liu, Q., Wang, X., Zhong, Z., Webb, J. and Schwartz, R.E., Bouklas, N., Ma, M., (2020). An Adhesive Hydrogel with “Load‐Sharing” Effect as Tissue Bandages for Drug and Cell Delivery. Advanced Materials, p.2001628.
  3. Yu, Y., Bouklas, N., Landis, C. M., & Huang, R. (2020). Poroelastic Effects on the Time-and Rate-Dependent Fracture of Polymer Gels. Journal of Applied Mechanics, 87(3).
  4. Liu, Z., Bouklas, N., & 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), 20190761.
  5. Li, B., & Bouklas, N. (2020). A variational phase-field model for brittle fracture in polydisperse elastomer networks. International Journal of Solids and Structures, 182, 193-204.

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., Wang, X., Lee, V.K., Dai, G., and Ma, M., (2019). Engineering transferrable microvascular meshes for subcutaneous islet transplantation. Nature communications, 10(1), 1-12.
  2. Mailand, E., Li, B., Eyckmans, J., Bouklas, N., & Sakar, M. S. (2019). Surface and bulk stresses drive morphological changes in fibrous microtissues. Biophysical journal, 117(5), 975-986.

2018

  1. Bouklas, N., Sakar, M. S., & 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., & Huang, R. (2018). A Linear Poroelastic Analysis of Time-Dependent Crack-Tip Fields in Polymer Gels. Journal of Applied Mechanics. 85(11).

prior to 2017

  1. Wu, Z., Bouklas, N., Liu, Y., Huang, R. (2016). Onset of swell-induced surface instability of hydrogel layers with depth-wise graded material properties. Mechanics of Materials, 105, 138-147.
  2. Bouklas, N., Landis, C. M., Huang, R. (2015). Effect of solvent diffusion on crack-tip fields and driving force for fracture of hydrogels, Journal of Applied Mechanics. 82, 081007.
  3. Bouklas, N., Landis, C. M., Huang, R. (2015). A nonlinear, transient finite element method for coupled solvent diffusion and large deformation of hydrogels. Journal of Mechanics and Physics of Solids. 79, 21-43.
  4. Wu, Z., Bouklas, N., 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), 578-587.
  5. Bouklas, N., Huang, R. (2012). Swelling kinetics of polymer gels: comparison of linear and nonlinear theories. Soft Matter. 8(31), 8194-8203.