Research Interests
Ten connected areas across computational mechanics, engineering data, numerical simulation, materials and optimization.
Current areas of interest
Self-Discovery Lab
Autonomous scientific research that selects questions, develops hypotheses, executes computational experiments and learns from reproducible evidence in mechanics, materials and scientific machine learning.
Physics-informed Machine Learning
Neural models that respect differential equations, constitutive behavior and thermodynamic constraints, with particular interest in solid mechanics and inverse problems.
Computational Solid Mechanics
Finite element modeling of deformation, contact, nonlinear response and material behavior, supported by experimental evidence.
Constitutive Modeling & Multiscale Transfer
Understanding how material realizations and internal states transfer between scales, geometries and loading paths.
Metamaterials & Inverse Design
Exploring how architecture controls mechanical response, and designing structures to meet specified stiffness, deformation and performance targets.
Digital Twins & System Identification
Connecting simulations to measurements through calibration, parameter identification and models of physical experiments.
Stochastic Systems & Uncertainty
Quantifying uncertainty, testing robustness and using stochastic modeling to understand the reliability of engineering predictions and decisions.
Engineering Data & Graph-based Models
Reproducible datasets and graph representations of physical structures, including geometry tracking and analysis of corrugated-board experiments.
Structural, Multi-objective & Learning-based Optimization
Structural and topology design, particle swarm methods, genetic hybrids and learned optimization strategies for exploring Pareto trade-offs in engineering design.
Materials, Manufacturing & Sustainable Packaging
Linking material properties, manufacturing quality and structural design to improve resource-efficient components and corrugated packaging.
