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Found 11 papers, 6 papers with code
Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines.
Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
Reliably reconstructing physical fields from sparse sensor data is a challenge that frequently arises in many scientific domains.
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data
Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses
This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets.
UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation
An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner.
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations.
A Bayesian Deep Learning Approach to Near-Term Climate Prediction
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction.
Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP.
Feature Importance in a Deep Learning Climate Emulator
From the perspective of climate dynamics, these findings suggest a dominant role for local processes and a negligible role for remote teleconnections at the spatial and temporal scales we consider.
