14 citations · 31 across the 18 of their papers we have counts for
17 papers · 1 filter
On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement
Baptiste Mathevon, Farah Cherfaoui, Amaury Habrard +1
Partial differential equation (PDE) discovery aims to identify from data the governing law of a physical system. Constituting a cornerstone of scientific advancement, it has become…
A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
Thien V. Nguyen, Amaury Habrard, Benjamin Guedj
Physics-informed machine learning (PIML) integrates mechanistic knowledge, typically in the form of partial differential equations (PDE), into data-driven models. Despite strong em…
From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction
Sami Marouani, Kamal Singh, Baptiste Jeudy +1
Accurate prediction of flow delay is essential for optimizing and managing modern communication networks. We investigate three levels of modeling for this task. First, we implement…
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks
Kamal Singh, Sami Marouani, Ahmad Al Sheikh +2
Reinforcement learning (RL) has been increasingly applied to network control problems, such as load balancing. However, existing RL approaches often suffer from lack of interpretab…
Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks
Antoine Caradot, Rémi Emonet, Amaury Habrard +2
Despite considerable scientific advances in numerical simulation, efficiently solving PDEs remains a complex and often expensive problem. Physics-informed Neural Networks (PINN) ha…
A Simple Way to Learn Metrics Between Attributed Graphs
Yacouba Kaloga, Pierre Borgnat, Amaury Habrard
The choice of good distances and similarity measures between objects is important for many machine learning methods. Therefore, many metric learning algorithms have been developed…