3 citations · 8 across the 10 of their papers we have counts for
12 papers
Optimal Transport, Timesteppers, Newton-Krylov Methods and Steady States of Collective Particle Dynamics
Hannes Vandecasteele, Nicholas Karris, Alexander Cloninger +1
Timesteppers constitute a powerful tool in modern computational science and engineering. Although they are typically used to advance the system forward in time, they can also be vi…
Enabling Local Neural Operators to perform Equation-Free System-Level Analysis
Gianluca Fabiani, Hannes Vandecasteele, Somdatta Goswami +2
Neural Operators (NOs) provide a powerful framework for computations involving physical laws that can be modelled by (integro-) partial differential equations (PDEs), directly lear…
Predicting the statistical error of analog particle tracing Monte Carlo
Vince Maes, Ignace Bossuyt, Hannes Vandecasteele +4
Large particle systems are often described by high-dimensional (linear) kinetic equations that are simulated using Monte Carlo methods for which the asymptotic convergence rate is…
Locating saddle points using gradient extremals on manifolds adaptively revealed as point clouds
A. Georgiou, H. Vandecasteele, J. M. Bello-Rivas +1
Steady states are invaluable in the study of dynamical systems. High-dimensional dynamical systems, due to a separation of time-scales, often evolve towards a lower dimensional man…
Pseudo-Marginal Approximation to the Free Energy in a Micro-Macro Markov Chain Monte Carlo Method
Hannes Vandecasteele, Giovanni Samaey
We introduce a generalised micro-macro Markov chain Monte Carlo (mM-MCMC) method with pseudo-marginal approximation to the free energy, that is able to accelerate sampling of the m…
Gentlest ascent dynamics on manifolds defined by adaptively sampled point-clouds
Juan M. Bello-Rivas, Anastasia Georgiou, Hannes Vandecasteele +1
Finding saddle points of dynamical systems is an important problem in practical applications such as the study of rare events of molecular systems. Gentlest ascent dynamics (GAD) i…