Deep learning surrogate models have shown promise in solving partial differential
equations (PDEs). Among them, the Fourier neural operator (FNO) achieves good accuracy …

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 …

High order discontinuous Galerkin methods allow accurate solutions through the use of high
order polynomials inside each mesh element. Increasing the polynomial order leads to high …

We address in this work the numerical modeling of fluid–structure interaction (FSI) with the
edge-based smoothed finite element method (ESFEM). We prove that the smoothed …

Mesh‐free properties are part of the superiority of cell‐based smoothed finite element
method (CS‐FEM), but have yet to be fully exploited for computational fluid dynamics. A …

We propose in this work a stabilization approach for semi-implicit coupling of viscoelastic
fluid–structure interaction (VFSI) using the cell-based smoothed finite element method (CS …

Bayesian calibration is widely used for inverse analysis and uncertainty analysis for complex
systems in the presence of both computer models and observation data. In the present work …

A useful approach to solve inverse problems is to pair the parameter-to-data map with a
stochastic dynamical system for the parameter, and then employ techniques from filtering to …

Multiphysics applications often require the use of intimately coupled solvers. The application
studied here makes use of an Eulerian solver to model fluid flow and combustion and a …

Modeling the time-dependent transient behavior of nuclear reactors with high-fidelity pin-
resolved detail has increased importance when the operating power of the reactor is …