Search Results for author: Dongxia Wu

Found 11 papers, 5 papers with code

Disentangled Multi-Fidelity Deep Bayesian Active Learning

1 code implementation7 May 2023 Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu

To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication.

Active Learning Gaussian Processes

Multi-fidelity Hierarchical Neural Processes

1 code implementation10 Jun 2022 Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

MF-HNP is flexible enough to handle non-nested high dimensional data at different fidelity levels with varying input and output dimensions.

Epidemiology Gaussian Processes

A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations

1 code implementation19 Aug 2021 Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley, Kevin G. Field, Dane Morgan

The system provides analysis of features observed in TEM including both static and dynamic properties using the YOLO-based defect detection module coupled to a geometry analysis module and a dynamic tracking module.

Defect Detection object-detection +1

Deep Bayesian Active Learning for Accelerating Stochastic Simulation

1 code implementation5 Jun 2021 Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations.

Active Learning

Multi defect detection and analysis of electron microscopy images with deep learning

no code implementations19 Aug 2021 Mingren Shen, Guanzhao Li, Dongxia Wu, YuHan Liu, Jacob Greaves, Wei Hao, Nathaniel J. Krakauer, Leah Krudy, Jacob Perez, Varun Sreenivasan, Bryan Sanchez, Oigimer Torres, Wei Li, Kevin Field, Dane Morgan

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis.

Defect Detection

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints

no code implementations28 Feb 2024 Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang

To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling

no code implementations29 Feb 2024 Ruijia Niu, Dongxia Wu, Kai Kim, Yi-An Ma, Duncan Watson-Parris, Rose Yu

Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources.

Gaussian Processes

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