16 citations · 18 across the 12 of their papers we have counts for
13 papers
SPLICE: Sparse Physics Library Identification and Compositional Expansion
Lucas Thil, Jesse Read, Guillaume Doquet +1
Neural operator splitting composes a library of neural operators, one per physical mechanism, into zero-shot simulators of new equations. Existing approaches, however, require thes…
Why Model Credibility Isn't Enough: -Rethinking Trust in Simulation Architectures
Romain Barbedienne, Adeline Lanugue, Rim Kaddah +6
Credibility of a simulation model is an important topic. Several approaches try to quantify the credibility of simulation. However, models are mostly assembled within a simulation…
Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
Lucas Thil, Jesse Read, Rim Kaddah +1
World models used for planning learn to predict how a scene evolves, yet they rarely hold a global view of how far a task has progressed. We argue that such a view should be part o…
STEP: Learning STructured Embeddings for Progressive Time Series
Lucas Thil, Jesse Read, Rim Kaddah +1
We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transitions such as degradation or task…
I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
Lucas Thil, Jesse Read, Rim Kaddah +1
Accurate remaining useful life (RUL) prediction hinges on the quality of health indicators (HIs), yet existing methods often fail to disentangle complex degradation mechanisms in m…
Uncertainty Quantification as a Complementary Latent Health Indicator for Remaining Useful Life Prediction on Turbofan Engines
Lucas Thil, Jesse Read, Rim Kaddah +1
Health Indicators (HIs) are essential for predicting system failures in predictive maintenance. While methods like RaPP (Reconstruction along Projected Pathways) improve traditiona…
Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cramér Surrogate
Simo Alami C., Rim Kaddah, Jesse Read +1
Distributional Reinforcement Learning (DistRL) improves upon expectation-based methods by modeling full return distributions, but standard approaches often remain far from parsimon…
Transferable Deep Metric Learning for Clustering
Simo Alami. C, Rim Kaddah, Jesse Read
Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric…
Conv-NILM-Net, a causal and multi-appliance model for energy source separation
Simo Alami C., Jérémie Decock, Rim Kaddah +1
Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become incre…
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
Simo Alami. C, Fernando Llorente, Rim Kaddah +2
Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal…
A Study of Solar Irradiance Prediction Error Impact on a Home Energy Management System
Mallek Mziou-Sallami, Rim Kaddah, Amira Ben Hamida
Nowadays, Energy Management Systems (EMS) are accessible for homes and buildings to optimize energy consumption especially when solar panels and batteries are installed. The intell…
Semi-Distributed Demand Response Solutions for Smart Homes
Rim Kaddah, Daniel Kofman, Fabien Mathieu +1
The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. It enables visibility and control on the various appliances that may consum…
Smart Fridge / Dumb Grid? Demand Dispatch for the Power Grid of 2020
Joel Mathias, Rim Kaddah, Ana Bušić +1
In discussions at the 2015 HICSS meeting, it was argued that loads can provide most of the ancillary services required today and in the future. Through load-level and grid-level co…