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
News Feed
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New Publication: Synthesizing Realistic Sand Assemblies with Denoising Diffusion
August 14, 2024
We are pleased to announce the acceptance and publication of our latest paper titled "Synthesizing Realistic Sand Assemblies with Denoising Diffusion in Latent Space" in the International Journal for Numerical and Analytical Methods in Geomechanics.
Authors: Nikolaos N. Vlassis, WaiChing Sun, Khalid A. Alshibli, Richard A. Regueiro
Access the paper here: http://doi.org/10.1002/nag.3818
This paper presents a novel approach for generating realistic sand grains using a denoising diffusion algorithm applied to point cloud data. The study also includes an open-source repository with synthetic sand grains and real synchrotron microcomputed tomography (SMT) scans https://doi.org/10.17632/fh8h4859nh.1.
Authors: Nikolaos N. Vlassis, WaiChing Sun, Khalid A. Alshibli, Richard A. Regueiro
Access the paper here: http://doi.org/10.1002/nag.3818
This paper presents a novel approach for generating realistic sand grains using a denoising diffusion algorithm applied to point cloud data. The study also includes an open-source repository with synthetic sand grains and real synchrotron microcomputed tomography (SMT) scans https://doi.org/10.17632/fh8h4859nh.1.
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Special Issue Launch: "Machine Learning in Multi-scale Modeling" in Applied Sciences MDPI - Call for Papers
May 22, 2024
Dear Colleagues,
We are excited to announce the launch of a peer-reviewed Special Issue on "Machine Learning in Multi-scale Modeling" for engineering and materials science. This Special Issue is part of the "Computing and Artificial Intelligence" section of Applied Sciences MDPI.
We invite you to submit original, high-quality research papers by 20 October 2024 on the following topics:
• Physics-informed ML for constitutive modeling in multi-scale structural and material systems
• The use of artificial neural networks (ANNs) to predict effective material properties
• Graph- and manifold-learning techniques in computational solid mechanics and material design
• Supervised and unsupervised ANN methods, including reduced-order simulations in computational mechanics
• The application of ANNs and ML-based optimization in the design ofmetamaterials relating to 3D-printing technologies
• Generative AI and deep learning-aided techniques for the multi-scale modeling and inverse design of materials and structural systems, including multiphysics composites and porous metamaterials
• Model-free approaches in computational mechanics
• Causal discovery for interpretable modeling
• Data-driven methods for solving partial differential equations (PDEs).
Yousef Heider (Institut für Baumechanik und Numerische Mechanik (IBNM), Leibniz Universität Hannover)
Nick Vlassis (Rutgers University)
Guest Editors
https://www.mdpi.com/journal/applsci/special_issues/NQT1D69Z48
We are excited to announce the launch of a peer-reviewed Special Issue on "Machine Learning in Multi-scale Modeling" for engineering and materials science. This Special Issue is part of the "Computing and Artificial Intelligence" section of Applied Sciences MDPI.
We invite you to submit original, high-quality research papers by 20 October 2024 on the following topics:
• Physics-informed ML for constitutive modeling in multi-scale structural and material systems
• The use of artificial neural networks (ANNs) to predict effective material properties
• Graph- and manifold-learning techniques in computational solid mechanics and material design
• Supervised and unsupervised ANN methods, including reduced-order simulations in computational mechanics
• The application of ANNs and ML-based optimization in the design ofmetamaterials relating to 3D-printing technologies
• Generative AI and deep learning-aided techniques for the multi-scale modeling and inverse design of materials and structural systems, including multiphysics composites and porous metamaterials
• Model-free approaches in computational mechanics
• Causal discovery for interpretable modeling
• Data-driven methods for solving partial differential equations (PDEs).
Yousef Heider (Institut für Baumechanik und Numerische Mechanik (IBNM), Leibniz Universität Hannover)
Nick Vlassis (Rutgers University)
Guest Editors
https://www.mdpi.com/journal/applsci/special_issues/NQT1D69Z48