The past decade has witnessed the superior power of deep neural networks (DNNs) in
applications across various domains. However, training a high-quality DNN remains a non …

Numerical simulation is crucial in scientific research. Visualizing simulation data helps
scientists understand data and discover connections between data. However, the …

Rapidly growing data sizes of scientific simulations pose significant challenges for
interactive visualization and analysis techniques. In this work, we propose a compact …

The use of distribution-based data representation to handle large-scale scientific datasets is
a promising approach. Distribution-based approaches often transform a scientific dataset …

科學家經常使用計算機模擬模型來研究物理現象. 為了研究物理現象中的不確定性會投過調整
初始參數內部的隨機變量來產生多個結果. 因此, 每個網格點是以模擬運行的多個數據值表示 …

Recently, ensemble simulations have been frequently used in various scientific domains,
including cosmology, oceanography, and fluid dynamics. To model scientific phenomena …

Almost all scientific data have uncertainties originating from different sources. Gaussian
process regression (GPR) models are a natural way to model data with Gaussian-distributed …

透過基於分布的資料表示法來處理大規模的科學資料集是一種新興且相當有潛力的方法.
這種資料表示法基本上是將科學資料集轉換為許多分布來表示, 並且每個分布皆由少量的樣本 …

Recent progress in high-performance computing now allows researchers to run extremely
high-resolution computational models, simulating detailed physical phenomena. Yet …

As the era of exascale computing approaches, the need for effective, scalable, and flexible
data reduction techniques is becoming more and more prominent. As discussed in the …