Dec 9, 2016 · 12:00 p.m.– 1:00 p.m. · Room 347, Visualization Lab
Speaker: Julian Kates-Harbeck
The prediction and avoidance of disruptions in tokamak fusion plasmas represents a key challenge on the way to stable energy production from nuclear fusion. A fusion plasma is a complex dynamical system with some unknown internal state which emits a time series of possibly high dimensional observable data that is captured by sensory diagnostics. Using such diagnostic data from past plasma shots with both disruptive and non-disruptive outcomes, we train a deep recurrent neural network to predict the onset of disruptions in an online setting. To deal with very large amounts of data and the need for iterative hyperparameter tuning, we also introduce a distributed training algorithm that runs on MPI clusters of GPU nodes and provides strong linear runtime scaling. Our approach demonstrates competitive predictive performance on experimental data from the JET tokamak, and we highlight promising avenues for extending our method to cross-tokamak prediction as well as to high-dimensional diagnostic data such as temperature and density profiles.
Julian grew up in Munich, Germany. He got his bachelor’s degree in physics at Stanford and later earned a master’s in computer science (with a focus on AI and machine learning) at the same university. In the period in between, he co-founded a tech startup, where he was responsible for hiring, product and strategy. He is currently pursuing a PhD in biophysics (specifically evolutionary dynamics on networks) at Harvard. For his studies there, he was awarded the National Science Foundation GRFP, Department of Defense NDSEG, and Department of Energy CSGF fellowships. Julian has written about biophysics, machine learning, astrophysics and plasma physics.
Dec 14, 2016 · 4:00 p.m.– 5:00 p.m. · Room 347, Visualization Lab
(Note: Web scraping with Python will be scheduled in early January.)
The study of dynamical systems has become a fundamental part of many scientific disciplines. While the subject was born in the 1600s with Newton's invention of differential equations it was not really until the invention of the computer that the study of dynamical systems really took off. Today implementations of integrators, visualization modules and optimization tools in python make it even easier for researcher to gain an intuition into complex non-linear systems. We will look at python tools and tricks for defining complex dynamical systems as functions, solving them with the scipy integrator, visualizing their phase space and delivering arbitrary instantaneous inputs. This will be a basic overview of the tools used by and developed in our lab to study non-linear dynamical systems.
Max Wilson is currently a postdoctoral fellow in the Molecular Biology Department. His work focuses on characterizing how molecular systems process and decode dynamic signals. His first experiences with python were at the early PrincetonPy meetings 2013 where he was captivated by the ease-of-use and flexibility of the language. He received his PhD from Princeton in 2015.
Jan 12, 2017 · 3:30 p.m.– 5:30 p.m. · Room 347, Visualization Lab
The Princeton Institute for Computational Science & Engineering (PICSciE) invites you to the re-opening of the Visualization Lab.
Join us on Thursday, January 12, 2017 anytime between 3:30 and 5:30 pm for refreshments and short demonstrations.
This event is open to the public.
Peter B. Lewis Library, Room 347
Washington Road and Ivy Lane, Princeton
Jan 18, 2017 · 4:00 p.m.– 5:00 p.m. · Room 347, Visualization Lab
The Environmental Systems Research Institute (Esri) has long used Python to develop its Geographic Information System (GIS) software and to provide scripting tools to users. A variety of geoprocessing tools are available as Python scripts, and script editing is supported within Esri’s ArcGIS for Desktop applications. This session will demonstrate Esri’s Python site package ArcPy within ArcGIS Pro, and also will access the ArcPy module outside of an Esri Desktop application. On-line resources to help users use ArcPy will also be explored.
Bill Guthe is a Geographic Information Systems Visualization Analyst in Research Computing / OIT. He provides support for ArcGIS, QGIS, ENVI, Imagine and other software packages. Wangyal Shawa, the GIS librarian, and Bill offer GIS training sessions each semester. They also teach a half-semester course, GIS for Public Policy, for the Woodrow Wilson School.
Feb 1, 2017 · 4:00 p.m.– 5:00 p.m. · Room 347, Visualization Lab
Speaker: Alexey Svyatkovskiy
Feb 15, 2017 · 4:00 p.m.– 5:00 p.m. · Room 347, Visualization Lab
We all need high-quality plots for data inspection, presentation, and publication. Matplotlib is a powerful Python library for visualizing data. Because it is so customizable, it can be challenging for beginners to find an efficient and reusable workflow. We will go over the structure of the matplotlib library and how to use it to generate beautiful custom plots.
Ben Deverett is a second-year graduate student from Toronto, Canada. He is studying molecular biology and neuroscience.
Mar 1, 2017 · 4:00 p.m.– 5:00 p.m. · Room 347, Visualization Lab
Speaker: Alexey Svyatkovskiy