Resimulating objects selected from the database Simulation infrastructure Natural to use simulations in the database to select objects for further study.

Slides:



Advertisements
Similar presentations
Shapelets Correlated with Surface Normals Produce Surfaces Peter Kovesi School of Computer Science & Software Engineering The University of Western Australia.
Advertisements

Characterizing Non- Gaussianities or How to tell a Dog from an Elephant Jesús Pando DePaul University.
Applications in Signal and Image Processing
DFT/FFT and Wavelets ● Additive Synthesis demonstration (wave addition) ● Standard Definitions ● Computing the DFT and FFT ● Sine and cosine wave multiplication.
Computer Vision for Human-Computer InteractionResearch Group, Universität Karlsruhe (TH) cv:hci Dr. Edgar Seemann 1 Computer Vision: Histograms of Oriented.
More MR Fingerprinting
Query Evaluation. An SQL query and its RA equiv. Employees (sin INT, ename VARCHAR(20), rating INT, age REAL) Maintenances (sin INT, planeId INT, day.
Non-linear matter power spectrum to 1% accuracy between dynamical dark energy models Matt Francis University of Sydney Geraint Lewis (University of Sydney)
Computer Graphics Recitation 6. 2 Motivation – Image compression What linear combination of 8x8 basis signals produces an 8x8 block in the image?
In The Beginning  N-body simulations (~100s particles) – to study Cluster formation  Cold collapse produces too steep a density profile (Peebles 1970)
Probabilistic video stabilization using Kalman filtering and mosaicking.
Basic Concepts and Definitions Vector and Function Space. A finite or an infinite dimensional linear vector/function space described with set of non-unique.
Comparative survey on non linear filtering methods : the quantization and the particle filtering approaches Afef SELLAMI Chang Young Kim.
Introduction to Wavelets
Identifying Interplanetary Shock Parameters in Heliospheric MHD Simulation Results S. A. Ledvina 1, D. Odstrcil 2 and J. G. Luhmann 1 1.Space Sciences.
Transforms: Basis to Basis Normal Basis Hadamard Basis Basis functions Method to find coefficients (“Transform”) Inverse Transform.
Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.
Computational Geophysics and Data Analysis
ENG4BF3 Medical Image Processing
Introduction to Monte Carlo Methods D.J.C. Mackay.
The Statistical Properties of Large Scale Structure Alexander Szalay Department of Physics and Astronomy The Johns Hopkins University.
10/21/03CS679 - Fall Copyright Univ. of Wisconsin Last Time Terrain Dynamic LOD.
Probability Theory and Random Processes
STAT 497 LECTURE NOTES 2.
The Nuts and Bolts of First-Principles Simulation Durham, 6th-13th December : DFT Plane Wave Pseudopotential versus Other Approaches CASTEP Developers’
1 Orthonormal Wavelets with Simple Closed-Form Expressions G. G. Walter and J. Zhang IEEE Trans. Signal Processing, vol. 46, No. 8, August 王隆仁.
Introduction to MCMC and BUGS. Computational problems More parameters -> even more parameter combinations Exact computation and grid approximation become.
CS654: Digital Image Analysis Lecture 3: Data Structure for Image Analysis.
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology Two Functions of Two Random.
CS654: Digital Image Analysis Lecture 15: Image Transforms with Real Basis Functions.
WAVELET TRANSFORM.
© Copyright 1992–2004 by Deitel & Associates, Inc. and Pearson Education Inc. All Rights Reserved. C How To Program - 4th edition Deitels Class 05 University.
Propagation of Charged Particles through Helical Magnetic Fields C. Muscatello, T. Vachaspati, F. Ferrer Dept. of Physics CWRU Euclid Ave., Cleveland,
© 2003, Carla Ellis Simulation Techniques Overview Simulation environments emulation exec- driven sim trace- driven sim stochastic sim Workload parameters.
Generic Approaches to Model Validation Presented at Growth Model User’s Group August 10, 2005 David K. Walters.
Module 1: Statistical Issues in Micro simulation Paul Sousa.
University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell Image processing.
Monte Carlo Methods.
Binary Stochastic Fields: Theory and Application to Modeling of Two-Phase Random Media Steve Koutsourelakis University of Innsbruck George Deodatis Columbia.
For a new configuration of the same volume V and number of molecules N, displace a randomly selected atom to a point chosen with uniform probability inside.
Part I: Image Transforms DIGITAL IMAGE PROCESSING.
Robustness of complex networks with the local protection strategy against cascading failures Jianwei Wang Adviser: Frank,Yeong-Sung Lin Present by Wayne.
Solution of the Inverse Problem for Gravitational Wave Bursts Massimo Tinto JPL/CIT LIGO Seminar, October 12, 2004 Y. Gursel & M. Tinto, Phys. Rev. D,
PROCESS MODELLING AND MODEL ANALYSIS © CAPE Centre, The University of Queensland Hungarian Academy of Sciences Statistical Model Calibration and Validation.
Real Gas Relationships
CHAPTER 5 SIGNAL SPACE ANALYSIS
Operations on Multiple Random Variables
1 8. One Function of Two Random Variables Given two random variables X and Y and a function g(x,y), we form a new random variable Z as Given the joint.
CSC2515: Lecture 7 (post) Independent Components Analysis, and Autoencoders Geoffrey Hinton.
ENEE631 Digital Image Processing (Spring'04) Basics on 2-D Random Signal Spring ’04 Instructor: Min Wu ECE Department, Univ. of Maryland, College Park.
Some thoughts on error handling for FTIR retrievals Prepared by Stephen Wood and Brian Connor, NIWA with input and ideas from others...
Winter 2011SEG Chapter 11 Chapter 1 (Part 1) Review from previous courses Subject 1: The Software Development Process.
1 LES of Turbulent Flows: Lecture 7 (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Fall 2014.
Joint Moments and Joint Characteristic Functions.
Geology 5600/6600 Signal Analysis 14 Sep 2015 © A.R. Lowry 2015 Last time: A stationary process has statistical properties that are time-invariant; a wide-sense.
One Function of Two Random Variables
V.M. Sliusar, V.I. Zhdanov Astronomical Observatory, Taras Shevchenko National University of Kyiv Observatorna str., 3, Kiev Ukraine
By Dr. Rajeev Srivastava CSE, IIT(BHU)
Initial conditions for N-body simulations Hans A. Winther ITA, University of Oslo.
CWR 6536 Stochastic Subsurface Hydrology Optimal Estimation of Hydrologic Parameters.
CSCI 631 – Foundations of Computer Vision March 15, 2016 Ashwini Imran Image Stitching Link: singhashwini.mesinghashwini.me.
3D Matter and Halo density fields with Standard Perturbation Theory and local bias Nina Roth BCTP Workshop Bad Honnef October 4 th 2010.
k is the frequency index
National Mathematics Day
k is the frequency index
8. One Function of Two Random Variables
Chapter 15: Wavelets (i) Fourier spectrum provides all the frequencies
9. Two Functions of Two Random Variables
16. Mean Square Estimation
8. One Function of Two Random Variables
Presentation transcript:

Resimulating objects selected from the database

Simulation infrastructure Natural to use simulations in the database to select objects for further study. Would be nice to automate this procedure so that it is relatively easy to make initial conditions for samples of object selected from the database. This requires a number of steps. In this talk I will focus mainly just one aspect -- which is how to choose the phase information for new simulations for the database. The choice of how phases are set up can help later when it comes to resimulating objects.

ICs Phase information

Generating Gaussian random fields I Useful to think of the task as consisting of two stages (1)Create a numerical approximation of a real Gaussian white noise field (i.e. a field with a constant power spectrum and a zero correlation function at non-zero lag). (2) Perform a convolution of this field with a real non-negative filter, to produce a Gaussian field with the desired power spectrum

Generating Gaussian random fields II Gaussian white noise fields have a special property: When expressed as a basis function expansion, the expansion coefficients are independent Gaussian random variables, provided the basis functions are mutually orthogonal. Use a pseudorandom number generator to generate a set of `independent’ expansion coefficients. The most familiar orthogonal basis functions for making initial conditions for structure formation calculations are planes waves. This basis set is not ideal for making multi-scale or resimulation initial conditions because the basis functions are not localised. There are better orthogonal basis sets …

Octree basis functions Establish a mapping between pseudorandom sequence and the octree cells. If the entire pseudorandom sequence is used (e.g numbers) then huge dynamic range is possible. Much bigger than needed for any one simulation.

Publishing phase information MXXL -3000Mpc/h Eagle Mpc Public multi-scale Gaussian white noise field. The phases can be specified by 5 integers. For example: Eagle volume phases are: [L16,(31250,23438,39063),S12]

How do you make resimulation initial conditions? Starting point is to take IC-GEN code and make minimal changes. This code has been used to make ics for cosmological volumes: e.g. MXXL Resimulations such as the Aquarius resimulations (Zeldovich approx) Phoenix resimulations (2lpt approx)

How do you make resimulation initial conditions?

Use octree basis functions as the building blocks Several advantages over using white noise field itself: (1)Each grid is completely independent of the others. The displacements add incoherently. (2)By construction the zeroth and first moments of the octree basis functions are identically zero. This means their contribution is highly localised which mitigates edge effects due to unwanted periodicity. For the inner grids need to decide on which grid to place each octree basis function.

How do you test the resimulations ics? By making the same initial conditions with two different methods: (1)Cosmological ICS – use a single huge Fourier transform (2)Resimulation ICS – use nested multiple smaller FTs Use the same particle load, N-body code, numerical parameters, and look at the final conditions to judge success. Taken a MW-mass halo from DOVE/COCO simulation to test the ics.

Eagle Volume: DOVE simulation Reference halo: M 200 =9.1x10 11 Msun/h Run with a Fourier transform! © Mark Lovell

Reference simulation Resimulation © Mark Lovell

The properties of the resimulated halo agree very closely with the reference calculation

Reference simulation Resimulation © Mark Lovell

To COCO resolution and beyond … From COCO simulation Resimulation © Mark Lovell

Resimulating an object found in a database Given the simulation and the position of the object – the phase information is easy to determine. Need to be able to trace a Lagrangian region from which it formed. –Requires particle data at the epoch of identification. Not necessarily at the full resolution. Making an economical Lagrangian region is hard, a more modest aim would be to make a generous region. Need a code to generate the particle load. A single compute node (with ~100 Gbytes RAM) needed to make most ICs.

Summary Taking care with setting the phases for simulations can add value There is new way to set the phase information using octree basis function expansions which has several advantages: – Consistent initial conditions over all scales – Ease of reproducibility – aiding sharing and publication of phase information. – Designed to allow very faithful resimulations Databases are a natural place to look for objects or regions to resimulate for many purposes. The ability to make resimulation initial conditions of objects/regions selected from the database would be nice! Some one has to do the work …