All Outputs (2)

Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization (2024)
Journal Article
Carlon, A. G., Espath, L., & Tempone, R. (2024). Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization. Optimization Methods and Software, https://doi.org/10.1080/10556788.2024.2339226

Using quasi-Newton methods in stochastic optimization is not a trivial task given the difficulty of extracting curvature information from the noisy gradients. Moreover, pre-conditioning noisy gradient observations tend to amplify the noise. We propos... Read More about Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization.

A bulk-surface continuum theory for fluid flows and phase segregation with finite surface thickness (2024)
Journal Article
Boschman, A., Espath, L., & van der Zee, K. G. (2024). A bulk-surface continuum theory for fluid flows and phase segregation with finite surface thickness. Physica D: Nonlinear Phenomena, 460, Article 134055. https://doi.org/10.1016/j.physd.2024.134055

In this continuum theory, we propose a mathematical framework to study the mechanical interplay of bulk-surface materials undergoing deformation and phase segregation. To this end, we devise a principle of virtual powers with a bulk-surface dynamics,... Read More about A bulk-surface continuum theory for fluid flows and phase segregation with finite surface thickness.