236 citations · 395 across the 11 of their papers we have counts for
11 papers
Denoising diffusion models for inverse design of inflatable structures with programmable deformations
Sara Karimi, Nikolaos N. Vlassis
Programmable structures are systems whose undeformed geometries and material property distributions are deliberately designed to achieve prescribed deformed configurations under sp…
A review on data-driven constitutive laws for solids
Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas +6
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-depe…
Synthesizing realistic sand assemblies with denoising diffusion in latent space
Nikolaos N. Vlassis, WaiChing Sun, Khalid A. Alshibli +1
The shapes and morphological features of grains in sand assemblies have far-reaching implications in many engineering applications, such as geotechnical engineering, computer anima…
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
Nikolaos N. Vlassis, WaiChing Sun
In this paper, we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generativ…
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Ruben Villarreal, Nikolaos N. Vlassis, Nhon N. Phan +5
Experimental data is costly to obtain, which makes it difficult to calibrate complex models. For many models an experimental design that produces the best calibration given a limit…
Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
Nikolaos N. Vlassis, WaiChing Sun
The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how th…
MD-inferred neural network monoclinic finite-strain hyperelasticity models for -HMX: Sobolev training and validation against physical constraints
Nikolaos N. Vlassis, Puhan Zhao, Ran Ma +2
We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal -HMX in the…
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Xiao Sun, Bahador Bahmani, Nikolaos N. Vlassis +2
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorith…
Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
Chen Cai, Nikolaos Vlassis, Lucas Magee +6
We present a SE(3)-equivariant graph neural network (GNN) approach that directly predicting the formation factor and effective permeability from micro-CT images. FFT solvers are es…
Sobolev training of thermodynamic-informed neural networks for smoothed elasto-plasticity models with level set hardening
Nikolaos N. Vlassis, WaiChing Sun
We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield…
Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity
Nikolaos Vlassis, Ran Ma, WaiChing Sun
This paper is the first attempt to use geometric deep learning and Sobolev training to incorporate non-Euclidean microstructural data such that anisotropic hyperelastic material ma…