A mechanics‐informed artificial neural network approach for learning constitutive laws
governing complex, nonlinear, elastic materials from strain–stress data is proposed. The …

We consider Bayesian inference for large-scale inverse problems, where computational
challenges arise from the need for repeated evaluations of an expensive forward model …

This paper is focused on the optimization approach to the solution of inverse problems. We
introduce a stochastic dynamical system in which the parameter-to-data map is embedded …

This paper proposes a strategy to solve the problems of the conventional s-version of finite
element method (SFEM) fundamentally. Because SFEM can reasonably model an analytical …

A high fidelity multi-physics Eulerian computational framework is presented for the simu-
lation of supersonic parachute inflation during Mars landing. Unlike previous investigations …

In this paper, we study efficient approximate sampling for probability distributions known up
to normalization constants. We specifically focus on a problem class arising in Bayesian …

Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …

A general‐purpose computational homogenization framework is proposed for the nonlinear
dynamic analysis of membranes exhibiting complex microscale and/or mesoscale …

Embedded or immersed boundary methods (IBM) are powerful mesh-based techniques that
permit to solve partial differential equations (PDEs) in complex geometries circumventing the …

A continuous forcing immersed boundary method (IBM) and a volume penalization method
are investigated to simulate compressible viscous flows past an isothermal or adiabatic solid …