Reduced Models and Uncertainty Quantification
DOI10.1007/978-3-030-30726-4_15zbMath1452.65259OpenAlexW2988544176MaRDI QIDQ5115082
Roland Pulch, Peter Benner, Sebastian Schöps, Yao Yue, Li-Hong Feng
Publication date: 29 June 2020
Published in: Mathematics in Industry (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-30726-4_15
uncertainty quantificationnanoelectronicsmodel order reductionmultiphysics systemscircuit-electromagnetic coupled system
PDEs in connection with optics and electromagnetic theory (35Q60) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Statistical mechanics of semiconductors (82D37) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Motion of charged particles (78A35) PDEs in connection with classical thermodynamics and heat transfer (35Q79) Statistical mechanics of nanostructures and nanoparticles (82D80) Numerical solution of discretized equations for initial value and initial-boundary value problems involving PDEs (65M22) Diffusive and convective heat and mass transfer, heat flow (80A19) Model reduction in optics and electromagnetic theory (78M34)
Uses Software
Cites Work
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
- Reduced Basis Modeling for Uncertainty Quantification of Electromagnetic Problems in Stochastically Varying Domains
- A Robust Algorithm for Parametric Model Order Reduction Based on Implicit Moment Matching
- UNCERTAINTY QUANTIFICATION FOR MAXWELL'S EQUATIONS USING STOCHASTIC COLLOCATION AND MODEL ORDER REDUCTION
- Parametric Model Order Reduction for Electro-Thermal Coupled Problems
- Dynamic Iteration for Coupled Problems of Electric Circuits and Distributed Devices
- A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
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