8 papers
Constitutive Priors for Inverse Design
Jinkyo Han, Bahador Bahmani
With recent advances in material synthesis and additive manufacturing, material systems can be designed to achieve prescribed mechanical responses. An important class of such probl…
Neural Operator Representation of Granular Micromechanics-based Failure Envelope
Jinkyo Han, Payam Poorsolhjouy, Bahador Bahmani
Micromechanics-based granular models are widely used to predict the failure behavior of porous and particulate materials, including concrete, soils, foams, and biological tissues.…
Conformal Quantile Regression for Neural Probabilistic Constitutive Modeling
Bahador Bahmani
Biological soft tissues exhibit substantial inter-subject variability, making the automation of constitutive material modeling essential for patient-specific analysis and design. S…
A Multimodal Conditional Mixture Model with Distribution-Level Physics Priors
Jinkyo Han, Bahador Bahmani
Many scientific and engineering systems exhibit intrinsically multimodal behavior arising from latent regime switching and non-unique physical mechanisms. In such settings, learnin…
A Multi-Fidelity Bayesian Neural Operator for Mechanics of Spinodal Metamaterial
Pu You, Hongshun Chen, Bahador Bahmani +1
Cellular metamaterials offer a vast design space for tailoring nonlinear mechanical responses, yet exploring this space with conventional modeling approaches is often infeasible or…
A Physics-informed Multi-resolution Neural Operator
Sumanta Roy, Bahador Bahmani, Ioannis G. Kevrekidis +1
The predictive accuracy of operator learning frameworks depends on the quality and quantity of available training data (input-output function pairs), often requiring substantial am…