Context:
We perform at SLAB controlled simulations of avalanche dynamics in view of improving risk assessment and hazard mapping. The simulations are based on the Material Point Method, a hybrid Eulerian-Lagrangian numerical method and finite strain elasto-plasticity [1]. Several simulations are performed to understand the effect of cohesion and friction on the avalanche velocity and runout.
![](../wp-content/uploads/2019/03/gaume2018-300x169.png)
![](../wp-content/uploads/2019/11/Screenshot-from-2019-11-04-15-14-56-217x300.png)
Fig. 1. Example of 3D MPM snow avalanche simulation and velocity profile.
Project:
The computational cost of these simulations is very high. Hence, Machine Learning (ML) appears as a promising approach to perform near-real time avalanche simulations. Thousands of MPM simulations performed at SLAB will be used to train a ML model similar to Holden et al. [2]. In this paper the system state is described as a vector of vertex positions. PCA is performed to reduce the representation of the state-space. Updates from previous states to later states are then efficiently computed using a Neural Network (NN). Our simulation results contain position and velocity of Lagrangian particles from the release to the arrest of the avalanche.
Resources provided to the student:
- Position and velocity of particles for 1000 MPM simulations (format .bgeo or .abc) for different values of mechanical properties of the snow
- Guidance on MPM and avalanche simulations
Outcome:
Perform a literature review of the field of data-driven physics based simulations. Evaluate the most appropriate ML framework to be used. Train and test the model on simple 2D avalanche geometries.
Prerequisites:
Background in ML algorithms, Python and c++ languages. Curiosity, desire to learn quickly and advance personal skills in this field.
References:
[1] Gaume et al. 2018. Dynamic anticrack propagation in snow. Nature Comm.
[2] Holden et al. 2017. Subspace Neural Physics: Fast Data-Driven Interactive Simulation. Proceedings of ACM.