Fahimeh Najafi is a PhD Candidate at the Condensed Matter Physics Group and NJORD, Department of Physics, UiO. She is also part of the CompSci doctoral programme. She studies the frictional properties of material coupled with machine learning methods.
Modeling of mechanical failure phenomena is important both in materials research and for our fundamental understanding of nature. On a microscopic scale, molecular dynamics (MD) simulations can be used to directly model the failure of materials. However, this can be time and resource-consuming to use on large sample spaces. Machine learning methods can learn the mapping between material structures and physical phenomena.
In this case, we focus on the relationship between the mechanical yield stress of α-quartz crystals with a porous layer under shear and tensile stress. We use simplex noise to create structures that can generate geometries that appear similar to natural terrains and surfaces. It is widely used for generating scenery in video games and animations. We further suggest that autoencoders can be used to encode the structures. This is particularly useful for generating new structures with modified yield stress. We are interested in creating an understanding of how these neural networks reflect the underlying physics with minimal direct guidance.
→ Read more about the seminar series in the dScience website
Programme
11:30 – Doors open and lunch is served
12:00 – "Modeling mechanical properties of material using neural networks" by Fahimeh Najafi (PhD Candidate, NJORD)
This event is open for all PhD candidates and postdocs. No registration needed.