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Found 11 papers, 2 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.
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data
World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming.
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.
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences
One among several advantages of measure transport methods is that they allow for a unified framework for processing and analysis of data distributed according to a wide class of probability measures.
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases.
Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing
Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design.
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response
There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source.
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.
A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing
The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems.
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.
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data
Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion.
