8 citations · 20 across the 28 of their papers we have counts for
4 papers · 1 filter
Unsupervised Physics-Informed Operator Learning through Multi-Stage Curriculum Training
Paolo Marcandelli, Natansh Mathur, Stefano Markidis +2
Solving partial differential equations remains a central challenge in scientific machine learning. Neural operators offer a promising route by learning mappings between function sp…
Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning
Paolo Marcandelli, Yuanchun He, Stefano Mariani +2
We introduce the Partitioned Hybrid Quantum Fourier Neural Operator (PHQFNO), a generalization of the Quantum Fourier Neural Operator (QFNO) for scientific machine learning. PHQFNO…
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions
Jonah Ekelund, Savvas Raptis, Vicki Toy-Edens +4
Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited o…
Decoding complexity: how machine learning is redefining scientific discovery
Ricardo Vinuesa, Paola Cinnella, Jean Rabault +10
As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an ess…