Category Archives: Materials science
MEPLS: statistical models of plastic deformation easier than ever
MEPLS is an open-source object-oriented framework for simulating the plastic deformation of materials based on the combination of stochastic processes and solid mechanics. I’ve developed it over my years of research in the statistical modeling of materials, although it’s still a work in progress. Its goal is to provide modular, efficient, and easy-to-use tools to build simulations for a broad range of physical scenarios. MEPLS abstracts away unnecessary complexity, allowing the user to focus on the physics of the model.
Statistical dynamics of creep: a Monte Carlo model of materials and earthquakes (||)
This a follow-up post to a previous one. As explained there, it is clear that if we overload a structure, it will collapse intermediately. Nonetheless, structures loaded well-bellow their maximum capacity might eventually fracture by damage accumulation. This is especially dangerous in civil structures such as bridges and damns and has also been related to natural catastrophic events such as landslides, cliff collapses, and some earthquakes and volcanic eruptions.
Here, we extend the Monte Carlo model of the previous post to allow it to simulate the approach to fracture.
Statistical dynamics of creep: a Monte Carlo model of materials and earthquakes (|)
It is clear that if we overload a structure, it will collapse intermediately. Nonetheless, structures loaded well-bellow their maximum capacity might eventually fracture by damage accumulation. This is especially dangerous in civil structures such as bridges and damns and has also been related to natural catastrophic events such as landslides, cliff collapses, and some earthquakes and volcanic eruptions. I implemented a stochastic model of plastic deformation based on the Kinetic Monte Carlo method able to shed light o this phenomenon…
Machine learning for predicting catastrophic events: the case of earthquakes
Many natural and social systems may exhibit abrupt, rare, very large events. Examples are material failure, bursting financial bubbles, epileptic seizures, volcano eruptions, power blackouts, etc. These phenomena typically occur unexpectedly and have catastrophic consequences for the system. Here we discuss the possibility of predicting material failure using machine learning techniques.