activity
20242026
most citedGaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

31 citations · 38 across the 5 of their papers we have counts for

collaborators

5 papers

physics.comp-ph2026

Structured force reformulation of many-body dispersion: towards effective atom--atom decomposition and surrogate modeling

Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas +2

We present a structured force reformulation of the many-body dispersion (MBD) model that enables a physically consistent decomposition of forces into pairwise components. By introd…

cond-mat.mtrl-sci2025

A Fully Discrete Element Approach for Modeling Vacuum Packed Particle Dampers

Pawel Chodkiewicz, Robert Zalewski, Jakub Lengiewicz

In this work, we investigate Vacuum-Packed Particle (VPP) dampers -- granular-core dampers offering tunable damping performance -- as a more sustainable alternative to conventional…

cs.LG2025★ 1 cited

Machine learning surrogate models of many-body dispersion interactions in polymer melts

Zhaoxiang Shen, Raúl I. Sosa, Jakub Lengiewicz +2

Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. H…

cs.CE2024★ 31 cited

Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

Saurabh Deshpande, Hussein Rappel, Mark Hobbs +2

Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is st…

cs.CE2024★ 6 cited

Quantum-informed simulations for mechanics of materials: DFTB+MBD framework

Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas +2

The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and unde…