Real-time reduced-order modeling of stochastic partial differential equations via time-dependent subspaces
From MaRDI portal
Publication:2194337
DOI10.1016/j.jcp.2020.109511zbMath1440.65012arXiv1910.04299OpenAlexW3023022637MaRDI QIDQ2194337
Publication date: 25 August 2020
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1910.04299
stochastic partial differential equationuncertainty quantificationreduced order modeltime-dependent subspaces
Stochastic partial differential equations (aspects of stochastic analysis) (60H15) Numerical solutions to stochastic differential and integral equations (65C30)
Related Items
Scalable \textit{in situ} compression of transient simulation data using time-dependent bases ⋮ A multifidelity deep operator network approach to closure for multiscale systems ⋮ Adaptive sparse interpolation for accelerating nonlinear stochastic reduced-order modeling with time-dependent bases ⋮ Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions ⋮ On-the-fly reduced order modeling of passive and reactive species via time-dependent manifolds ⋮ Computing Sensitivities in Evolutionary Systems: A Real-Time Reduced Order Modeling Strategy
Uses Software
Cites Work
- Unnamed Item
- A convergence study for SPDEs using combined polynomial chaos and dynamically-orthogonal schemes
- On the equivalence of dynamically orthogonal and bi-orthogonal methods: theory and numerical simulations
- Fundamental limitations of polynomial chaos for uncertainty quantification in systems with intermittent instabilities
- A dynamically bi-orthogonal method for time-dependent stochastic partial differential equations. II: Adaptivity and generalizations
- A dynamically bi-orthogonal method for time-dependent stochastic partial differential equations. I: Derivation and algorithms
- Adaptive ANOVA decomposition of stochastic incompressible and compressible flows
- Multi-level Monte Carlo finite element method for elliptic PDEs with stochastic coefficients
- Dynamically orthogonal field equations for continuous stochastic dynamical systems
- Multi-element probabilistic collocation method in high dimensions
- The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications
- Long-term behavior of polynomial chaos in stochastic flow simulations
- Mean field dynamics of fermions and the time-dependent Hartree-Fock equation
- A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems
- Dual dynamically orthogonal approximation of incompressible Navier Stokes equations with random boundary conditions
- Sparse grid collocation schemes for stochastic natural convection problems
- Dynamic mode decomposition of numerical and experimental data
- Stochastic Domain Decomposition via Moment Minimization
- Multilevel Monte Carlo Path Simulation
- Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures
- Turbulence and the dynamics of coherent structures. I. Coherent structures
- Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
- Reduced-order description of transient instabilities and computation of finite-time Lyapunov exponents
- An Extension of MATLAB to Continuous Functions and Operators
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- Quasi-Monte Carlo Finite Element Methods for a Class of Elliptic Partial Differential Equations with Random Coefficients
- From snapshots to modal expansions – bridging low residuals and pure frequencies
- An observation-driven time-dependent basis for a reduced description of transient stochastic systems
- Error Analysis of the Dynamically Orthogonal Approximation of Time Dependent Random PDEs
- Multi-fidelity modelling of mixed convection based on experimental correlations and numerical simulations
- A minimization principle for the description of modes associated with finite-time instabilities
- Dynamical Low‐Rank Approximation
- High-Order Collocation Methods for Differential Equations with Random Inputs
- A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
This page was built for publication: Real-time reduced-order modeling of stochastic partial differential equations via time-dependent subspaces
