research
∙
06/22/2023
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Recent years have seen many successful applications of machine learning ...
share
research
∙
05/25/2023
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
Quantifying the uncertainty of quantities of interest (QoIs) from physic...
research
∙
11/09/2022
QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression
We present a new convolution layer for deep learning architectures which...
research
∙
09/13/2022
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting
Least squares regression is a ubiquitous tool for building emulators (a....
research
∙
09/13/2022
Fast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems
Approximate solutions to large least squares problems can be computed ef...
research
∙
04/03/2022
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets
Recent advances in modeling large-scale complex physical systems have sh...
research
∙
03/25/2022
Simultaneous Identification and Denoising of Dynamical Systems
In recent years there has been a push to discover the governing equation...
research
∙
02/02/2022
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 d...
research
∙
01/31/2022
GenMod: A generative modeling approach for spectral representation of PDEs with random inputs
We propose a method for quantifying uncertainty in high-dimensional PDE ...
research
∙
01/29/2022
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study
In recent years, identification of nonlinear dynamical systems from data...
research
∙
05/27/2021
Neural Network Training Using ℓ_1-Regularization and Bi-fidelity Data
With the capability of accurately representing a functional relationship...
research
∙
05/04/2021
Deterministic matrix sketches for low-rank compression of high-dimensional simulation data
Matrices arising in scientific applications frequently admit linear low-...
research
∙
04/15/2021
Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification
A ubiquitous challenge in design space exploration or uncertainty quanti...
research
∙
03/02/2021
Task-parallel in-situ temporal compression of large-scale computational fluid dynamics data
Present day computational fluid dynamics simulations generate extremely ...
research
∙
05/27/2020
Sparse Identification of Nonlinear Dynamical Systems via Reweighted ℓ_1-regularized Least Squares
This work proposes an iterative sparse-regularized regression method to ...
research
∙
02/11/2020
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation
Due to their high degree of expressiveness, neural networks have recentl...
research
∙
02/11/2019
Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach
Topology optimization under uncertainty (TOuU) often defines objectives ...
research
∙
12/29/2017