Inelastic deformation processes with random parameters -- methods of analysis and design.

DOI10.1016/S0045-7825(03)00264-0zbMath1119.74627OpenAlexW1968573427MaRDI QIDQ1418250

Ioannis St. Doltsinis

Publication date: 19 January 2004

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0045-7825(03)00264-0

zbMATH Keywords

Stochastic analysisRobustnessProcess designDeformation processes

Mathematics Subject Classification ID

Related Items (12)

A priori error estimate of stochastic Galerkin method for optimal control problem governed by stochastic elliptic PDE with constrained control ⋮ A sparse grid stochastic collocation upwind finite volume element method for the constrained optimal control problem governed by random convection diffusion equations ⋮ A sequential nonlinear interval number programming method for uncertain structures ⋮ Perturbation-based stochastic FE analysis and robust design of inelastic deformation processes ⋮ Stochastic perturbation method for optimal control problem governed by parabolic PDEs with small uncertainties ⋮ A Sparse Grid Stochastic Collocation Discontinuous Galerkin Method for Constrained Optimal Control Problem Governed by Random Convection Dominated Diffusion Equations ⋮ Unnamed Item ⋮ Numerical simulation of the forging process ⋮ Stochastic Galerkin method for optimal control problem governed by random elliptic PDE with state constraints ⋮ A priori error estimate of perturbation method for optimal control problem governed by elliptic PDEs with small uncertainties ⋮ Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations ⋮ Robust design of nonlinear structures using optimization methods

Cites Work

• Design-oriented analysis of structures. A unified approach
• Reliability assessment for sheet metal forming operations.
• Interactive stability-oriented reliability-based design optimization
• Parallel solution methods for stochastic finite element analysis using Monte Carlo simulation
• Finite element analysis based on stochastic Hamilton variational principle
• Process design and sensitivity analysis in metal forming
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