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Found 5 papers, 2 papers with code
A Deep Learning Approach to Probabilistic Forecasting of Weather
We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information about forecast quantities; and a density estimation step that uses the probabilistic machine learning technique of normalizing flows to compute the joint probability density of reduced predictors and forecast quantities.
Inferring Morphology and Strength of Magnetic Fields From Proton Radiographs
Proton radiography is an important diagnostic method for laser plasma experiments, and is particularly important in the analysis of magnetized plasmas.
Plasma Physics Instrumentation and Detectors
Simultaneous Reconstruction and Uncertainty Quantification for Tomography
Tomographic reconstruction, despite its revolutionary impact on a wide range of applications, suffers from its ill-posed nature in that there is no unique solution because of limited and noisy measurements.
Targeted Adaptive Design
Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters.
OS-net: Orbitally Stable Neural Networks
We introduce OS-net (Orbitally Stable neural NETworks), a new family of neural network architectures specifically designed for periodic dynamical data.
