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Found 10 papers, 4 papers with code
PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model
The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.
PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model
To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates.
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost.
A Bi-fidelity DeepONet Approach for Modeling Uncertain and Degrading Hysteretic Systems
Nonlinear systems, such as with degrading hysteretic behavior, are often encountered in engineering applications.
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets
Recent advances in modeling large-scale complex physical systems have shifted research focuses towards data-driven techniques.
Neural Network Training Using $\ell_1$-Regularization and Bi-fidelity Data
These bi-fidelity strategies are generalizations of transfer learning of neural networks that uses the parameters learned from a large low-fidelity dataset to efficiently train networks for a small high-fidelity dataset.
Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning
The physics model used in this study comprises of a monolithically coupled system of acoustic and elastic wave equations, known as the wave propagation in fluid-solid and their interface (WpFSI) problem.
Uncertainty Quantification of Locally Nonlinear Dynamical Systems using Neural Networks
A standard nonlinear solver for them with sampling-based methods for uncertainty quantification incurs significant computational cost for estimating the statistics of the response.
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation
In the former approach, a neural network model mapping the inputs to the outputs of interest is trained based on the low-fidelity data.
Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach
To tackle this difficulty, we here propose an optimization approach that generates a stochastic approximation of the objective, constraints, and their gradients via a small number of adjoint (and/or forward) solves, per optimization iteration.
Optimization and Control Numerical Analysis
