Leslie A. Rose Dept. of Mechanical Engineering · South Dakota Mines
CEAD develops computational methods for materials that fracture, crush, stick and reorganize. The group studies which physical mechanisms a model must retain, and how approximation error affects the decision made from a simulation.
Output
Rendered from raw simulation output and from the group's own teaching material
How electric and magnetic fields interact with microstructure, interfaces and geometry, and how those interactions determine actuation, adhesion and degradation in soft composites.
5 papers 3 researchers Two lineages: coarse graining of field–material coupling since 2016, soft-composite design and adhesion since 2025
The full argumentHow matrix cracking, interface debonding, particle fracture and contact interactions compete to control strength, toughness, localization and residual load capacity.
19 papers 1 researcher
The full argumentHow residuals, error estimates and targeted high-fidelity solves can determine when a reduced or learned model is accurate enough for inference, optimization or design.
11 papers
The full argumentA graduate course whose spine is error control, an open textbook written as it is taught, twelve executable notebooks, research software with users, and undergraduate research that has reached conference presentations and a first-authored manuscript.
Prashant K. Jha
Assistant Professor of Mechanical Engineering
What a model must keep in order to be trusted, and where it is worth spending computation.
Henry Anderson
Master's Researcher
Interfacial cohesion between stiff particles and a hyperelastic matrix.
Ian Galloway
Master's Researcher
Joint material–structural design of hard-magnetic soft materials.
Caleb Oien
Master's Researcher
Design and 3D printing of soft materials.
Particle-resolved and multifidelity modeling of granular assemblies whose particles deform, break and rearrange under load.
Choosing where to put material and where to put magnetization, at the same time, so a soft structure moves the way a design problem asks it to.
Residual-based estimation and correction of neural-operator error in forward simulation, Bayesian inference and topology optimization.
Peridynamics-based discrete element method for granular media with deformable, complex-shaped particles.
Joint material and structural topology optimization for field-responsive soft materials, built on FEniCSx.
Python implementations of neural operators and the residual-correction schemes, with the notebooks used to test them
Openings
We are recruiting graduate and undergraduate researchers interested in computational mechanics, fracture, and scientific machine learning. Prior HPC or FEM experience helps but is not required.