Arthur Girard

Arthur Girard

Data-Driven Engineer & Researcher — PhD from ETH Zürich.
Specializing in Machine Learning, computational mechanics, and surrogate modeling.

I leverage expertise in complex data modeling, Python, and advanced analytics to build robust data workflows. From synthetic data pipelines to ML-driven surrogate models, I turn computationally intensive processes from days into minutes.

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Education

2018 — 2025
PhD, Mechanical Engineering
ETH Zürich

Thesis: "Machine Learning-Based Surrogate Modeling for Plasticity and Damage in Composite Materials"

2016 — 2019
MSc, Mechanical Engineering
ETH Zürich
2012 — 2016
BSc, Mechanical Engineering
EPFL · Lausanne

Publications

Machine Learning-Based Surrogate Modeling for Plasticity and Damage in Composite Materials
International Journal of Solids and Structures · 2025
End-to-end surrogate model for predicting plasticity and damage in composite materials, validated against high-fidelity simulations.
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Cost-Effective High-Throughput Compression Testing for Sheet Metals
International Journal of Solids and Structures · 2025
Novel miniature specimen methodology enabling more accurate constitutive modeling for automotive forming simulations.
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