Component-wise reduced order model lattice-type structure design

  title={Component-wise reduced order model lattice-type structure design},
  author={Sean McBane and Youngsoo Choi},

Efficient space-time reduced order model for linear dynamical systems in Python using less than 120 lines of code

This work presents for the first time the derivation of the space-time Petrov-Galerkin projection for linear dynamical systems and its corresponding block structures and derives an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.

Reduced order models for Lagrangian hydrodynamics

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks towards learning a forward and an inverse solution operator of partial differential equations (PDEs), and provides a speed-up of 120000 times compared to a Gaussian priorbased inverse modeling approach while also delivering more accurate inverse results.

Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models

Certified data-driven physics-informed greedy auto-encoder simulator

An adaptive greedy sampling algorithm integrated with a physics-informed error indicator is introduced to search for optimal training samples on the basis of parameter space for optimal model performance, outperform-ing the conventional predefined uniform sampling.



Topology optimization of hierarchical lattice structures with substructuring

Accelerating design optimization using reduced order models

The acceleration approach is demonstrated in topology optimization examples, including both compliance minimization and stress-constrained problems, where it achieves a tremendous reduction and speed-up when a traditional preconditioner fails to achieve a considerable reduction in the number of linear solve iterations.

Design optimization using hyper-reduced-order models

It is shown in this paper that an additional approximation of the objective function is required by the construction of a surrogate objective using radial basis functions, and the proposed method is illustrated with two applications: the shape optimization of a simplified nozzle inlet model and the design optimized of a chemical reaction.

Simple, accurate surrogate models of the elastic response of three-dimensional open truss micro-architectures with applications to multiscale topology design

This work provides simple, accurate surrogate models of the homogenized linear elastic response of the isotruss, the octet truss, and the ORC truss based on high-fidelity continuum finite element analyses that are relatively accurate over the full range of relative densities, in contrast to analytical models in the literature.

Topology optimization for concurrent design of layer-wise graded lattice materials and structures

Conservative model reduction for finite-volume models

Concurrent topology optimization design of structures and non-uniform parameterized lattice microstructures

Additional parameters are introduced to define the microstructure unit cell patterns and their non-uniform distribution, which avoids expensive iterative numerical homogenization calculations during topology optimization and results in an easier modelling of structure designs as well.

Finite-Element-Mesh Based Method for Modeling and Optimization of Lattice Structures for Additive Manufacturing

A parameterization modeling method based on finite element mesh to create complex large-scale lattice structures for AM is presented, and a corresponding approach for size optimization of lattice