42 citations · 155 across the 16 of their papers we have counts for
17 papers
Data-Driven Cohesive Zone Modeling within the Generalized Standard Materials Framework
Sida Hao, Jinkyo Han, Bahador Bahmani
Cohesive zone models are widely used to describe fracture and interfacial failure, yet most formulations prescribe problem-specific analytical traction-separation laws together wit…
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.…
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…
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 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…
Neural Chaos: A Spectral Stochastic Neural Operator
Bahador Bahmani, Ioannis G. Kevrekidis, Michael D. Shields
Building surrogate models with uncertainty quantification capabilities is essential for many engineering applications where randomness, such as variability in material properties,…
A Resolution Independent Neural Operator
Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis +1
The Deep Operator Network (DeepONet) is a powerful neural operator architecture that uses two neural networks to map between infinite-dimensional function spaces. This architecture…
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…
Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials
Bahador Bahmani, WaiChing Sun
We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse exper…
Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
Bahador Bahmani, Hyoung Suk Suh, WaiChing Sun
Conventional neural network elastoplasticity models are often perceived as lacking interpretability. This paper introduces a two-step machine learning approach that returns mathema…
Manifold embedding data-driven mechanics
Bahador Bahmani, WaiChing Sun
This article introduces a new data-driven approach that leverages a manifold embedding generated by the invertible neural network to improve the robustness, efficiency, and accurac…
Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
Bahador Bahmani, WaiChing Sun
This paper presents a PINN training framework that employs (1) pre-training steps that accelerates and improve the robustness of the training of physics-informed neural network wit…
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…
An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
Bahador Bahmani, WaiChing Sun
We present a hybrid model/model-free data-driven approach to solve poroelasticity problems. Extending the data-driven modeling framework originated from Kirchdoerfer and Ortiz (201…