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Found 15 papers, 6 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.
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations.
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.
QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression
We present a new convolution layer for deep learning architectures which we call QuadConv -- an approximation to continuous convolution via quadrature.
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.
Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles
A new, machine learning-based approach for automatically generating 3D digital geometries of woven composite textiles is proposed to overcome the limitations of existing analytical descriptions and segmentation methods.
GenMod: A generative modeling approach for spectral representation of PDEs with random inputs
Using results from PDE theory on coefficient decay rates, we construct an explicit generative model that predicts the polynomial chaos coefficient magnitudes.
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study
In this work, we investigate and compare the performance of several local and global smoothing techniques to a priori denoise the state measurements and numerically estimate the state time-derivatives to improve the accuracy and robustness of two sparse regression methods to recover governing equations: Sequentially Thresholded Least Squares (STLS) and Weighted Basis Pursuit Denoising (WBPDN) algorithms.
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.
Sparse Identification of Nonlinear Dynamical Systems via Reweighted $\ell_1$-regularized Least Squares
The aim of this work is to improve the accuracy and robustness of SINDy in the presence of state measurement noise.
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
Time-dependent global sensitivity analysis with active subspaces for a lithium ion battery model
Renewable energy researchers use computer simulation to aid the design of lithium ion storage devices.
Computational Physics Numerical Analysis
