CEAD (Computational Engineering Analysis and Design) Lab
Our vision: applying CEAD to engineering problems (P) to develop solutions (X). We share research software, open educational resources, and computational tutorials.
- Prashant K. Jha - @prashjha (PI)
- Henry Anderson — @HenryAnderson-Mines
- Ian Galloway — @ian-g8
- Caleb Oien — @CalebXathos
- Alison Reeves — @areeves6
CEAD Lab Website: ceadpx.github.io
- Functional materials and design: modeling and design of field-responsive soft materials.
- Fracture, contact, and complex materials: peridynamics, particle deformation and breakage, and the mechanics of heterogeneous materials.
- Reliable scientific machine learning: neural operators, residual-based error correction, and the integration of learned surrogates with numerical solvers.
- Predictive computational methods: multiscale modeling, uncertainty quantification, Bayesian inference, and error estimation and control.
Across these efforts, we emphasize predictive accuracy and computational efficiency, with particular attention to estimating and controlling errors.
We develop open resources that connect physical reasoning, mathematical formulation, implementation, and verification:
- Finite Element Methods: open book and course materials — an evolving resource developed alongside our graduate FEM course, connecting mathematical foundations and variational formulations with heat-transfer and solid-mechanics problems.
- FEniCS and FEniCSx tutorials — computational examples covering mesh generation, Poisson equations, nonlinear solvers, and linear and nonlinear elasticity.
- PeriDEM — a peridynamics-based discrete element method for simulating deformable and breakable particles with contact.
- Neural operators — implementations and experiments supporting our work on operator learning and surrogate modeling.
- MatTO — a FEniCSx-based framework for joint material and topology optimization of stimulus-responsive soft materials, including magnetic elastomers and liquid crystal elastomers.
Explore the repositories for code, examples, and documentation. Questions, bug reports, and contributions to both research and educational resources are welcome.