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Found 6 papers, 1 papers with code
Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization
Recreating complex, high-dimensional global fields from limited data points is a grand challenge across various scientific and industrial domains.
Using Ornstein-Uhlenbeck Process to understand Denoising Diffusion Probabilistic Model and its Noise Schedules
The aim of this short note is to show that Denoising Diffusion Probabilistic Model DDPM, a non-homogeneous discrete-time Markov process, can be represented by a time-homogeneous continuous-time Markov process observed at non-uniformly sampled discrete times.
Mitigation of Spatial Nonstationarity with Vision Transformers
However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context.
Predictive Scale-Bridging Simulations through Active Learning
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements.
MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning
Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of noise, imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains, and pores in an image, which make their identification difficult.
Modeling nanoconfinement effects using active learning
At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.
