Hadas Hochster

Hadas Hochster is a Ph.D. candidate at the lab. She started working in the lab in 2018 as a research assistant, mainly designing and conducting experiments on CFRP composites and characterization of the microstructure using optical and SEM microscopes. Her master's thesis includes predicting failure envelopes using different failure criteria (strain invariant failure theory, cohesive-zone model) using the PHFGMC micromechanical model. In addition, in her master she started using artificial neural networks (ANNs) for embedding the PHFGMC micromechanical model in structural, multiscale finite element analyses. The method was applied to predict low-velocity impact of composite laminated plates and stiffened-panels. In her PhD, she continues implementing AI, machine-learning methods as surrogate models for structural analyses of composite materials, allowing the embedding of micromechanical methods in multiscale finite element analyses.

Education

2022-present: PhD in mechanical engineeringMechanical Engineering School, Engineering Faculty, Tel Aviv UniversityAdvisor: Professor Rami Haj-Ali

2020-2022: M.Sc. in mechanical engineeringMechanical Engineering School, Engineering Faculty, Tel Aviv UniversityAdvisor: Professor Rami Haj-Ali

2016-2020: Double major degree: B.Sc. in mechanical engineering, B.A humanities unitMechanical Engineering School, Engineering Faculty, Tel Aviv University

Complete Reference List of Published Work:

1. Hochster, H., et al., Refined nonlinear micromechanical models using artificial neural networks for multiscale analysis of laminated composites subject to low-velocity impact. International Journal of Solids and Structures, 2023. 264: p. 112123.

Projects: 

Surrogate PHFGMC Micromechanical Models for Multiscale Analysis: AI-Enhanced Low-Velocity Impact Analysis of Composite plates