Understanding how materials and structures deform, damage, and fail through computation, experiment, and data. Our research sits at the intersection of solid mechanics, computational modeling, machine learning, and experimental mechanics.
Learning history-dependent material behavior
Material response depends on the path taken to reach a state, not just the state itself. We develop recurrent neural operators and learned constitutive models that capture this history dependence of materials and models can be implemented with finite-element solvers to achieve multiscale high fidelity accuracy with affordable computational cost.
Failure, damage & inverse characterization
We study how materials accumulate irreversible deformation, localize damage, and ultimately fail. Topics include viscoplasticity, power-law creep, quasibrittle fracture, and crack-band modeling.
Mechanical tests rarely measure constitutive parameters directly. We use Bayesian inference, surrogate modeling, and full-field data to infer material behavior from indirect structural measurements such as indentation, and we treat the uncertainty in that inference as part of the result rather than an afterthought.
Architected materials & metal additive manufacturing
Architected materials generate rich, nonuniform fields even under simple macroscopic loading. We use that complexity as both a mechanics problem and a source of information for model discovery. We print metallic lattice in-house by laser powder bed fusion, then characterize and model them from unit cell to component. Interests include numerical homogenization of inelastic lattices, microstructure design, the defects that printing actually produces, and the process–property relationships.
Nonlinear structures & multiphysics
We work on nonlinear structural mechanics, including plates and shells, large deformation, stability and buckling, nonlinear dynamics, and coupled thermal–electromagnetic–mechanical systems.