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02/23/2023
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Recovering a globally accurate complex physics field from limited sensor...
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02/20/2023
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
Perception of the full state is an essential technology to support the m...
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01/17/2023
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Temperature field prediction is of great importance in the thermal desig...
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03/29/2022
Parameterized Consistency Learning-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis in Aerospace Engineering
Polynomial chaos expansion (PCE) is a powerful surrogate model-based rel...
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03/10/2022
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
As a powerful way of realizing semi-supervised segmentation, the cross s...
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03/08/2022
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
Few-shot segmentation enables the model to recognize unseen classes with...
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02/14/2022
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
Temperature field reconstruction is essential for analyzing satellite he...
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02/14/2022
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
For the temperature field reconstruction (TFR), a complex image-to-image...
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09/26/2021