[BOOK][B] Uncertainty quantification: theory, implementation, and applications
RC Smith - 2024 - SIAM
Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …
engineering, and biological applications using mechanistic models. From a broad …
FastSVD-ML–ROM: A reduced-order modeling framework based on machine learning for real-time applications
Digital twins have emerged as a key technology for optimizing the performance of
engineering products and systems. High-fidelity numerical simulations constitute the …
engineering products and systems. High-fidelity numerical simulations constitute the …
Adaptive sparse interpolation for accelerating nonlinear stochastic reduced-order modeling with time-dependent bases
Stochastic reduced-order modeling based on time-dependent bases (TDBs) has proven
successful for extracting and exploiting low-dimensional manifold from stochastic partial …
successful for extracting and exploiting low-dimensional manifold from stochastic partial …
Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations
The implementation of data assimilation often struggles with low computational efficiency
due to the complexity of high-fidelity numerical modeling and the large size of the …
due to the complexity of high-fidelity numerical modeling and the large size of the …
Randomized greedy magic point selection schemes for nonlinear model reduction
An established way to tackle model nonlinearities in projection-based model reduction is via
relying on partial information. This idea is shared by the methods of gappy proper …
relying on partial information. This idea is shared by the methods of gappy proper …
Structure-preserving hyper-reduction and temporal localization for reduced order models of incompressible flows
A novel hyper-reduction method is proposed that conserves kinetic energy and momentum
for reduced order models of the incompressible Navier-Stokes equations. The main …
for reduced order models of the incompressible Navier-Stokes equations. The main …
Randomized quasi-optimal local approximation spaces in time
J Schleuß, K Smetana, L Ter Maat - SIAM Journal on Scientific Computing, 2023 - SIAM
We target time-dependent partial differential equations (PDEs) with heterogeneous
coefficients in space and time. To tackle these problems, we construct reduced …
coefficients in space and time. To tackle these problems, we construct reduced …
Modeling of MEMS Electrothermal Microgripper employing POD-DEIM and POD method
Eletrothermal microgrippers are nowadays commonly used as they are small in size, low
cost and easy to manufacture. Modeling of these microgrippers is challenging as most of the …
cost and easy to manufacture. Modeling of these microgrippers is challenging as most of the …
A training set subsampling strategy for the reduced basis method
We present a subsampling strategy for the offline stage of the Reduced Basis Method. The
approach is aimed at bringing down the considerable offline costs associated with using a …
approach is aimed at bringing down the considerable offline costs associated with using a …
Forecasting high-dimensional spatio-temporal systems from sparse measurements
This paper introduces a new neural network architecture designed to forecast high-
dimensional spatio-temporal data using only sparse measurements. The architecture uses a …
dimensional spatio-temporal data using only sparse measurements. The architecture uses a …