computational modelling and simulation
DESCRIPTION
COMPUTATIONAL MODELLING and SIMULATION. Modelling and Scientific Computing Research Group Speakers: H. Ruskin/L.Tuohey. INTRODUCTION. HISTORY: 1953 MANIAC simulates liquid ! Monte Carlo influence in Los Alamos Dynamic developments 1957,1959….. …..Computational Science…..!. - PowerPoint PPT PresentationTRANSCRIPT
1
COMPUTATIONAL MODELLING and
SIMULATIONModelling and Scientific Computing
Research Group
Speakers: H. Ruskin/L.Tuohey
2
INTRODUCTION
• HISTORY: 1953
MANIAC simulates liquid !
• Monte Carlo influence in Los Alamos
• Dynamic developments 1957,1959…..
…..Computational Science…..!
3
CM &S. - Motivation
• EXACTLY soluble/Analytical Methods• IF NOT? …. Approximate?• Simulation “Essentially” exact
“Essence” of problem
copes with intractability
can “test” theories and experiment
direct route micro macro
4
CM &S. - applications
• Examples
Atoms … to galaxies, polymers, artificial life, brain and cognition, financial markets and risk,traffic flow and transportation,
ecological competition, environmental hazards,……….
Nonlinear, Non-equilibrium...
5
EXPERIMENT, THEORY & COMPUTER SIMULATION
E X P E R IM E N TA LR E S U L TS
E X P E R IM E N T
R E A L W O R L D B U IL D P H Y S IC A LM O D E L
TE S TS O FM O D E L S
TE S TS O FTH E O R IE S
C O M P A R E
E X A C T R E S U L TSF O R M O D E L
S IM U L A TIO N
TH E O R E TIC A L P R E D IC TIO N S
C O N S TR U C T A P P R O X .TH E O R IE S
TH E O R E TIC A LM O D E L
6
CORE ACTIVITIES "PHASES" -in aCOMPUTATIONAL SCIENCE Investigation
Development of the maths. model Development of the algorithm for
numeric solution Implementation of the algorithm Generation of solutions e.g.
through numerical simulation ofthe scientific phenomenon
Representation of the computedresults e.g. through graphicalvisualization
Interpretation of results ( with adegree of "healthy scepticism")and model validation
KEY ASPECTS
of
SCIENTIFICCOMPUTING
7
COMPLEX SYSTEMS
• Size - billions of elements, events etc. many variables,
• Changes - dynamics
• Lack of Sequence or pattern
• Instability - (Non-equilibrium)
• Non-constant cause-effect (Non-linearity)
• Global vs local changes
etc.
8
COMPLEXITY 2 - WHAT SORT OF TOOLS?
• Cellular Automata- “chess-board”
• Monte Carlo- random numbers
• Lattice Gas Models - Lattice-based, conservation laws “Fluid” Models
• SOC - self-driven catastrophes
• Neural Networks - content addressable memory
• Genetic Algorithms- model evolution by natural selection; mutation, selection
9
MODELS - why do we need them?
• TO PICTURE HOW SYSTEM WORKS
• TO REMOVE NON-ESSENTIALS , called reducing the “degrees of freedom” (simple model first)
• TO TEST IT
• TO TRY SOMETHING DIFFERENT
…….cheaply and quickly
10
CATEGORIES of COMPUTATION
• numerical analysis (simplification prior to computation)
• symbolic manipulation (mathematical forms e.g. differentiation, integration, matrix algebra etc.)
• simulation (essential elements - minimum of pre- analysis)
• data collection/analysis• visualisation
11
SIMULATION LEVELS
• BRIDGING KNOWLEDGE GAP
Idealised Model Algorithm Results
• GOALS - SIMPLE LAWS
- PLAUSIBILITY
- MEW METHODS/MODELS
• DIRECT / INDIRECT
• PHENOMENOLOGY vs DETAIL
12
NONLINEAR SYSTEMS
• MOST Natural Phenomena - Nonlinear
e.g. Weather patterns, turbulent flows of liquids, ecological systems = geometrical
• CHAOS
e.g. unbounded growth or population explosion cannot continue indefinitely - LIMIT = sustainable environment
13
NON-EQUILIBRIUM SYSTEMS
• Inherently UNSTABLE
• FEW THEORIES - often simulation leads the way
• EXAMPLE - froth coarsening
14
HOW FROTHS EVOLVE
15
EXAMPLE- Financial Markets, volatility/ turbulence
Sequence number
1654
1567
1480
1393
1306
1219
1132
1045
958
871
784
697
610
523
436
349
262
175
88
1
Te
chn
ica
l1400
1200
1000
800
600
400
200
0
16
- Finance 2
• KEY EVENTS 07/97 - 11/97 Roller-Coaster Asian Crisis
14/09/98 -South American BAD news
23/07/99 -plunge after highs/Greenspan address
12/12/00 -U.S. Supreme Court judgement on Election Result
28/03/01 -cut in Fed. Reserve rate not enough
18/09/01 -post Sept. 11th
17
EXAMPLEImmunological Response
Antigens
Macrophage (M)
Helper (H)
B cells
ViralInfectedcell(V)
Cytotoxic Killer cells(C)
Cell mediated arm of Immune-response
Humoral arm .. etc.
18
CURRENT- Immunology2
• Physical Space – Stochastic C.A– Microscopic– Macroscopic
• Shape Space– N-Dimensional Space that models affinity rules
and repertoire size
19
SHAPE SPACE FORMULATION
V
V
V
V
R
introduced to predict repertoire size
Sphere of Influence for Immune System Components
20
ALGORITHMS - MD, MC etc.
• MD - many particle - build dynamics from known interactions
• MC - most probable outcome (random numbers)
• CA - discrete dynamical systems. Finite states. Local updates.
• FD/BV - solns. D.E.’s - e,g. predictor/ corrector algorithms
21
Example - TRAFFIC FLOW as a C.A. Model
c0 0 0 b b
0 a a
Major-stream
AVMinor-stream
22
Algorithms - contd
• PROBLEM - open-ended
• CORRECT/ENOUGH?
Basics - compare with known results
- Orders of Magnitude
- Errors/Limits
- Extensions?
Coherent Story
23
LANGUAGES ETC.
• PROCEDURAL/ FUNCTIONAL, OBJECT-ORIENTED - Fortran , C (change state or memory of machine by sequence of statements); LISP, Mathematica, Maple (function takes I/P to give O/P); C++, JAVA (Program = structured collection of objects)
• PLATFORM
24
NUMBERS, PRETTY PICTURES and INSIGHT
• NUMBERS, PICTURES AGREEMENT? - not enough
e.g. Simulation of river networks as a Random Walk. Path of Walker = Meandering of River
……..Why?