Assistant Professor
Mechanical & Aerospace Engineering
| Phone: | 848-445-5517 |
| Email: | nick.vlassis@rutgers.edu |
| Office: | ENG A-200 |
Office Hours: By Appointment
Website: https://nickvlassis.com
Education
- Ph.D., Civil Engineering & Engineering Mechanics, Columbia University, 2021
- M.S., Civil Engineering, National Technical University of Athens, 2017
- B.S., Civil Engineering, National Technical University of Athens, 2017
Research Interests
Computational solid mechanics leveraging machine learning to address challenges in multiscale material modeling and design. Interpretable machine learning, geometric deep learning, and generative AI techniques are utilized for constitutive modeling and microstructure characterization with non-Euclidean descriptors while respecting physics constraints. Current projects focus on predicting mechanical behavior and designing materials across scales with the help of AI, aiming to surpass human modeling capacity while grounded in fundamental mechanics principles.
Selected Publications
- N. Vlassis, Ran Ma, W.C. Sun, Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity, Computer Methods in Applied Mechanics and Engineering 371: 113299, doi:10.1016/j.cma.
2020.113299, 2020. - N. Vlassis, W.C. Sun, Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening, Computer Methods in Applied Mechanics and Engineering 377: 113695, doi:10.1016/j.cma.
2021.113695, 2021. - N. Vlassis, W.C. Sun, Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models, Journal of Applied Mechanics 89.2:021003, https:/
/doi.org/10.1115/1.4052684, 2021. - N. Vlassis, P. Zhao, R. Ma, T. Sewell, W.C. Sun, Molecular dynamics inferred transfer learning models for finite-strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints, International Journal for Numerical Methods in Engineering, Volume 123, Issue 17, Featured Cover, https://doi.org/10.
1002/nme.6992, 2022. - N. Vlassis, W.C. Sun, Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity, Computer Methods in Applied Mechanics and Engineering, 404: 115768, https://doi.org/10.
1016/j.cma.2022.115768, 2023. - N. Vlassis, W.C. Sun, Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties, Computer Methods in Applied Mechanics and Engineering, 413:116126, https
://doi.org/10.1016/j.cma.2023. 116126, 2023.
(For a full list of publications visit Google Scholar)