Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Pavliotis, Grigorios"'
We propose a method utilizing physics-informed neural networks (PINNs) to solve Poisson equations that serve as control variates in the computation of transport coefficients via fluctuation formulas, such as the Green--Kubo and generalized Einstein-l
Externí odkaz:
http://arxiv.org/abs/2410.00278
Autor:
Da Costa, Lancelot, Da Costa, Nathaël, Heins, Conor, Medrano, Johan, Pavliotis, Grigorios A., Parr, Thomas, Meera, Ajith Anil, Friston, Karl
Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some non-trivial temporal correlation structure, which renders the SDE non-
Externí odkaz:
http://arxiv.org/abs/2409.15532
Autor:
Wehlitz, Nathalie, Sadeghi, Mohsen, Montefusco, Alberto, Schütte, Christof, Pavliotis, Grigorios A., Winkelmann, Stefanie
This work proposes stochastic partial differential equations (SPDEs) as a practical tool to replicate clustering effects of more detailed particle-based dynamics. Inspired by membrane-mediated receptor dynamics on cell surfaces, we formulate a stocha
Externí odkaz:
http://arxiv.org/abs/2407.18952
We study interacting particle systems driven by noise, modeling phenomena such as opinion dynamics. We are interested in systems that exhibit phase transitions i.e. non-uniqueness of stationary states for the corresponding McKean-Vlasov PDE, in the m
Externí odkaz:
http://arxiv.org/abs/2406.11725
We consider weakly interacting diffusions on the torus, for multichromatic interaction potentials. We consider interaction potentials that are not H-stable, leading to phase transitions in the mean field limit. We show that the mean field dynamics ca
Externí odkaz:
http://arxiv.org/abs/2406.04884
We consider nonparametric statistical inference on a periodic interaction potential $W$ from noisy discrete space-time measurements of solutions $\rho=\rho_W$ of the nonlinear McKean-Vlasov equation, describing the probability density of the mean fie
Externí odkaz:
http://arxiv.org/abs/2404.16742
Overdamped Langevin dynamics are reversible stochastic differential equations which are commonly used to sample probability measures in high-dimensional spaces, such as the ones appearing in computational statistical physics and Bayesian inference. B
Externí odkaz:
http://arxiv.org/abs/2404.12087
We consider the problem of estimating unknown parameters in stochastic differential equations driven by colored noise, which we model as a sequence of Gaussian stationary processes with decreasing correlation time. We aim to infer parameters in the l
Externí odkaz:
http://arxiv.org/abs/2312.15975
The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such prediction
Externí odkaz:
http://arxiv.org/abs/2312.03147
Autor:
Evangelou, Nikolaos, Giovanis, Dimitrios G., Kevrekidis, George A., Pavliotis, Grigorios A., Kevrekidis, Ioannis G.
Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In thi
Externí odkaz:
http://arxiv.org/abs/2310.19039