88 citations · 876 across the 55 of their papers we have counts for
10 papers · 1 filter
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection
Luca Magri, Filippo Leveni, Giacomo Boracchi
We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defin…
PIF: Anomaly detection via preference embedding
Filippo Leveni, Luca Magri, Giacomo Boracchi +1
We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantage…
Control-aware echo state networks (Ca-ESN) for the suppression of extreme events
Alberto Racca, Luca Magri
Extreme event are sudden large-amplitude changes in the state or observables of chaotic nonlinear systems, which characterize many scientific phenomena. Because of their violent na…
Short and Straight: Geodesics on Differentiable Manifolds
Daniel Kelshaw, Luca Magri
Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and pr…
Physics-Informed Long Short-Term Memory for Forecasting and Reconstruction of Chaos
Elise Özalp, Georgios Margazoglou, Luca Magri
We present the Physics-Informed Long Short-Term Memory (PI-LSTM) network to reconstruct and predict the evolution of unmeasured variables in a chaotic system. The training is const…
A physics-based domain adaptation framework for modelling and forecasting building energy systems
Zack Xuereb Conti, Ruchi Choudhary, Luca Magri
State-of-the-art machine-learning-based models are a popular choice for modeling and forecasting energy behavior in buildings because given enough data, they are good at finding sp…
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics
Alberto Racca, Luca Magri
An approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, c…
Automatic-differentiated Physics-Informed Echo State Network (API-ESN)
Alberto Racca, Luca Magri
We propose the Automatic-differentiated Physics-Informed Echo State Network (API-ESN). The network is constrained by the physical equations through the reservoir's exact time-deriv…
Physics-Informed Echo State Networks
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve su…
Learning ergodic averages in chaotic systems
Francisco Huhn, Luca Magri
We propose a physics-informed machine learning method to predict the time average of a chaotic attractor. The method is based on the hybrid echo state network (hESN). We assume tha…