Search Results for author:
Found 9 papers, 6 papers with code
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering.
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
The MLP embedding is propitious to more sparse input, while the others benefit from spatial information preservation and perform better with the increase of observation data.
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Then, a self-supervised learning method for training the physics-driven deep multi-fidelity model (PD-DMFM) is proposed, which fully utilizes the physics characteristics of the engineering systems and reduces the dependence on large amounts of labeled low-fidelity data in the training process.
Consistency regularization-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis
To alleviate this problem, this paper proposes a consistency regularization-based deep polynomial chaos neural network (Deep PCNN) method, including the low-order adaptive PCE model (the auxiliary model) and the high-order polynomial chaos neural network (the main model).
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
However, the wrong pseudo labeling information generated by cross supervision would confuse the training process and negatively affect the effectiveness of the segmentation model.
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
This paper proposes a contrastive enhancement approach using latent prototypes to leverage latent classes and raise the utilization of similarity information between prototype and query features.
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
To solve the above problem, this paper proposes an unsupervised method, i. e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing temperature field and quantifying the aleatoric uncertainty caused by data noise.
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise.
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive capability bottleneck of most deep surrogate models, which also exists in surrogate for thermal analysis and design.
