5 citations · 7 across the 4 of their papers we have counts for
6 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…
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…
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…
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…