Optimization of Stockholm TMA + nuero-optimizer + analysis of seismological data
||Optimization of Stockholm TMA + nuero-optimizer + analysis of seismological data|
||SNIC Medium Compute|
||Tatiana Polishchuk <firstname.lastname@example.org>|
||2020-02-01 – 2021-02-01|
The proposal consists of the following three research projects::
Optimization of Stockholm TMA in 4d scale
Simulation of novel neuromorphic computing hardware for combinatorial optimization
Processing of seismological data
Project 1 is a continuation of our previous project Optimization of Stockholm TMA in 4dscale (SNIC 2019/3-34), a collaborative project LIU+LFV (Luftfartsverket), in which we work on optimization of the aircraft arrival routes within Stockholm airspace. The problem is formulated as a Mixed-Integer program (MIP) and is implemented using Python and Gurobi solver.
Project 2 is a collaboration between LiU and University of California, Santa Barbara, in which we develop novel computing hardware for combinatorial optimization. We will simulate the neuro-optimizer hardware behavior on different combinatorial problems to determine which hardware should be produced to maximize the potential performance. The computational difficulty comes from the need to simulate the performance of the hardware on NP-hard problems, as they are the most interesting from the practical perspective.
In project 3 we will analyze historical observations of significant seismological events recorded on seismic stations and produce estimations of various statistical indicators. While the process of analysis takes only a couple of minutes on an ordinary PC for a single combination of station and event, the main computational difficulty arises in the huge amount of data: observations for more than a thousand of events recorded on hundreds of stations are needed to be processed.