19 citations · 36 across the 7 of their papers we have counts for
7 papers
Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks
Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely
Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical results, such as Hopfield's model of ass…
Coarse-graining nonequilibrium diffusions with Markov chains
Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely
We investigate nonequilibrium steady-state dynamics in both continuous- and discrete-state stochastic processes. Our analysis focuses on planar diffusion dynamics and their coarse-…
Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates
Ramón Nartallo-Kaluarachchi, Shashanka Ubaru, Małgorzata J Zimoń +4
Sequential generative models conditioned on uncertain rewards are central to AI-driven scientific discovery, yet the epistemic uncertainty they inherit from imperfect reward estima…
Nonequilibrium physics of brain dynamics
Ramón Nartallo-Kaluarachchi, Morten L. Kringelbach, Gustavo Deco +2
Information processing in the brain is coordinated by the dynamic activity of neurons and neural populations at a range of spatiotemporal scales. These dynamics, captured in the fo…
From reductionism to realism: Holistic mathematical modelling for complex biological systems
Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain Goriely
At its core, the physics paradigm adopts a reductionist approach, aiming to understand fundamental phenomena by decomposing them into simpler, elementary processes. While this stra…
Decomposing force fields as flows on graphs reconstructed from stochastic trajectories
Ramón Nartallo-Kaluarachchi, Paul Expert, David Beers +4
Disentangling irreversible and reversible forces from random fluctuations is a challenging problem in the analysis of stochastic trajectories measured from real-world dynamical sys…