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GrainLearning

An artificial brain for interpreting and accelerating physics-based simulations of granular materials

GrainLearning calibration workflow

How to keep dikes safe with rising sea levels? Why are ripples formed in sand? What can we prepare for landing on Mars? At the center of these questions is the understanding of how the grains, as a self-organizing material, collide, flow, or get jammed and compressed. State-of-the-art algorithms allow for simulating millions of grains individually in a computer. However, these simulations can take very long, and the big data about particle motion is very difficult to interpret and generalize, say from a simulation of avalanches to free-standing sandcastles.

GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, which is also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.

Participating organisations

Netherlands eScience Center
University of Twente
Natural Sciences & Engineering
Natural Sciences & Engineering

Output

Team

Luisa Orozco
Luisa Orozco
eScience Research Engineer
Netherlands eScience Center
HC
Hongyang Cheng
Principal investigator
University of Twente
Rena Bakhshi
Rena Bakhshi
eScience Coordinator
Netherlands eScience Center
Elena Ranguelova
Elena Ranguelova
Tech Lead
Netherlands eScience Center
Aron Jansen
Aron Jansen
eScience Research Engineer
Netherlands eScience Center
HRE
Hans-Christian Ruiz Euler
KT
Klaus Thoeni
Advisor
The University of Newcastle
BC
Bruno Chareyre
Advisor
Université Grenoble Alpes

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Updated 8 months ago
Pending