Computational Mechanics & Artificial Intelligence Lab
Nick Napoleon Vlassis is an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Rutgers University.
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Geometric Deep Learning for Computational Mechanics Part I: Anisotropic Hyperelasticity
Geometric Deep Learning for Computational Mechanics Part II: Graph Embedding for Interpretable Multiscale Plasticity
Sobolev Training of Thermodynamic-Informed Neural Networks for Interpretable Elasto-Plasticity Models with Level Set Hardening
Molecular Dynamics Inferred Transfer Learning Models for Finite-Strain Hyperelasticity of Monoclinic Crystals: Sobolev Training and Validations Against Physical Constraints
Component-Based Machine Learning Paradigm for Discovering Rate-Dependent and Pressure-Sensitive Level-Set Plasticity Models
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
nick.vlassis@rutgers.edu
NYC,
2025.
2025.
Computational Solid Mechanics, Generative Artificial Intelligence, and Graph Learning
Nick N. Vlassis received his bachelor’s degree in civil engineering and a master’s degree in structural design and analysis of structures from the National Technical University of Athens, Greece in 2017. He received his PhD degree from Columbia University in 2021 and was selected to receive the Mindlin Scholarship for his research on machine learning and data-driven methods for solid mechanics. After graduation, he worked as a postdoctoral research scientist at Columbia University.
He has been an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Rutgers University since the fall of 2023. His group, Mech[Ai] integrates computational solid mechanics with interpretable machine learning, graph learning, and generative AI, with particular emphasis on multiscale modeling.