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Found 9 papers, 1 papers with code
Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators
To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle accelerators.
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge.
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Learning Abstract Task Representations
A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation.
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
The Recommendation System for Spectroscopic follow-up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment.
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
A General Approach to Domain Adaptation with Applications in Astronomy
The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications.
Transfer Learning in Astronomy: A New Machine-Learning Paradigm
In contrast, a new generation of techniques is emerging where predictive models can take advantage of previous experience to leverage information from similar tasks.
Conceptual Domain Adaptation Using Deep Learning
We introduce a search framework to correctly align high-level representations when training deep networks; such framework leads to the notion of conceptual --as opposed to representational-- domain adaptation.
