Outputs (4)

Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty (2024)
Journal Article
Bartuska, A., Espath, L., & Tempone, R. (2025). Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty. Statistics and Computing, 35(1), Article 12. https://doi.org/10.1007/s11222-024-10544-z

Finding the optimal design of experiments in the Bayesian setting typically requires estimation and optimization of the expected information gain functional. This functional consists of one outer and one inner integral, separated by the logarithm fun... Read More about Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty.

Deep NURBS—admissible physics-informed neural networks (2024)
Journal Article
Saidaoui, H., Espath, L., & Tempone, R. (2024). Deep NURBS—admissible physics-informed neural networks. Engineering with Computers, 40, 4007-4021. https://doi.org/10.1007/s00366-024-02040-9

In this study, we propose a new numerical scheme for physics-informed neural networks (PINNs) that enables precise and inexpensive solutions for partial differential equations (PDEs) in case of arbitrary geometries while strongly enforcing Dirichlet... Read More about Deep NURBS—admissible physics-informed neural networks.

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, 39(6), 1352-1382. 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.