activity
20202024
most citedMultiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)

177 citations · 255 across the 5 of their papers we have counts for

collaborators

5 papers

physics.class-ph2024

Measurement, self-similarity, and TNT equivalence of blasts from exploding wires

Ahmad Morsel, Filippo Masi, Panagiotis Kotronis +1

Reduced-scale experiments offer a controlled and safe environment for studying the effects of blasts on structures. Traditionally, these experiments rely on the detonation of solid…

physics.class-ph2024★ 3 cited

miniBLAST: a novel experimental setup for laboratory testing of structures under blast loads

Ahmad Morsel, Filippo Masi, Emmanuel Marché +3

We present a novel experimental setup called miniBLAST, which enables systematic and repeatable laboratory tests of structures subjected to blast loads. The explosive source is bas…

cs.CE2022★ 63 cited

Evolution TANN and the identification of internal variables and evolution equations in solid mechanics

Filippo Masi, Ioannis Stefanou

Data-driven and deep learning approaches have demonstrated to have the potential of replacing classical constitutive models for complex materials. Yet, the necessity of structuring…

cond-mat.mtrl-sci2021★ 177 cited

Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)

Filippo Masi, Ioannis Stefanou

The mechanical behavior of inelastic materials with microstructure is very complex and hard to grasp with heuristic, empirical constitutive models. For this purpose, multiscale, ho…

math.NA2020★ 12 cited

Scaling laws for the rigid-body response of masonry structures under blast loads

Filippo Masi, Ioannis Stefanou, Victor Maffi-Berthier

The response of masonry structures to explosions can be hardly investigated relying only on numerical and analytical tools. Experimental tests are of paramount importance for impro…