Transcript Robert Boyle and His Views on God

The Bible’s Perspective on
Intelligent Design
Powerpoint slides can be found at
www.acgr.org
Intelligent design (ID) is the proposition that "certain features of the universe
and of living things are best explained by an intelligent cause, not an undirected
process such as natural selection."[1][2] It is a form of creationism and a
contemporary adaptation of the traditional teleological argument for the
existence of God, presented by its advocates as "an evidence-based scientific
theory about life's origins" rather than "a religious-based idea". It avoids
specifying that the hypothesized intelligent designer is God.[3] Its leading
proponents are associated with the Discovery Institute, a politically conservative
think tank,[n 1][4] and believe the designer to be the Christian God.[n 2]
Wikipedia
ID Premise
ID seeks to redefine science in a fundamental way that
would invoke supernatural explanations, a viewpoint
known as theistic science. It puts forward a number of
arguments, the most prominent of which are irreducible
complexity and specified complexity, in support of the
existence of a designer.[5] The scientific community
rejects the extension of science to include supernatural
explanations in favor of continued acceptance of
methodological naturalism,[n 3][n 4][6][7] and has rejected
both irreducible complexity and specified complexity for
a wide range of conceptual and factual flaws.[8][9][10][11]
Wikipedia
Questions
•
•
•
•
•
What is science?
What is engineering?
What is design?
What is innovation?
What are the differences between the
above?
Confusing Definitions
High School Physics Teacher:
“Engineers are those who make things based on the
discoveries given to them by scentists”
Dictionary: engineering=science of useful processes,
phenomena, and devices (so is physics unconcerned
with useful things???)
Applied Physics=when physics addresses useful things
“Engineering is an art” (art is not always about useful
things)
Science: knowledge (scientia in Latin)
Inventor
Engineer
Scientist
Rocket Scientist
Science and Engineering
Clearly Defined
“Science seeks to understand what is;
Engineering seeks to create what never
was.”
—Theodore von Kármán
Definitions from Webster’s 1828
Dictionary
• Design: To delineate, to plan, to purpose,
or to intend with a plan in mind for
expression
• Innovate: To change by making
something new
Engineering
• Engineering requires “design” and
“science” to “innovate” and then
organizing, procuring, and producing the
product
Engineering and Science: A
Biblical Perspective
Deut 29:29 The secret things belong to the Lord
our God; but those things which are revealed
belong to us and to our children for ever.
God’s revelation
to mankind to date
secret
things
God’s revelation
to mankind that
we have not
received yet
Creator Reveals His Character
Romans 1:20 for ever since the creation
of the world, His invisible nature and
attributes, that is his eternal power and
divinity have been clearly made
intelligible and clearly discernible in the
things that have been made.
Engineering and Science have a goal:
to study the creation in order to understand the Creator
Issues in Design
•
•
•
•
•
•
Beauty: symmetric proportions that please the eye
Order: methodical arrangement of things
Sense: perceptions that impress the body
Purpose: object to be reached as an expected end
Cognition: knowledge from a personal view
Simplicity: singleness, uncompounded, basic
denominator
• Information: intelligence or knowledge that is
communicated
• Complexity/Systemization: assemblage or
collection in ordered manner
• Designer: intelligence implied outside the designed
system
Engineering and Science: A
Biblical Perspective
Deut 29:29 The secret things belong to the Lord
our God; but those things which are revealed
belong to us and to our children for ever.
Spiritual Laws
from the
Intelligent
Designer
Physical Laws and
Spiritual Laws
Known
Physical Laws and
Spiritual Laws
Unknown
Language of Design: Mathematics
• Design Optimization is a systematic way of
searching for the maxima and minima of
functions related to design subject to some
constraints.
Typical problem formulation:
Given
Find
Min
Subject to
p………………..parameters of the problem
d………………..the design variables of the problem
f(d,p)…………...objective function
g(d,p)≤0………..inequality constraints
h(d,p)=0………..equality constraints
dL≤ d ≤ dU………lower and upper bounds
• Example: Point stress design for minimum weight
13
Given
Find
Min
S.t.
w, N
t
t (i.e., weight)
σ – σcrit = N/(w t) – σY ≤0
tL ≤ t ≤ tU
w
t
N
N
Definitions
Design variables (d): A design variable is a parameter that is controllable by the
designer (eg., thickness, material, etc.) and are often bounded by maximum and minimum
values. Sometimes these bounds can be treated as constraints.
Constraints (g, h): A constraint is a condition that must be satisfied for the design to be
feasible. Examples include physical laws, constraints can reflect resource limitations, user
requirements, or bounds on the validity of the analysis models. Constraints can be used
explicitly by the solution algorithm or can be incorporated into the objective using Lagrange
multipliers.
Objectives (f): An objective is a numerical value or function that is to be maximized or
minimized. For example, a designer may wish to maximize profit or minimize weight. Many
solution methods work only with single objectives. When using these methods, the designer
normally weights the various objectives and sums them to form a single objective.
Models: The designer must also choose models to relate the constraints and objectives to
design variables. They may include finite element analysis, reduced order metamodels, etc.
Reliability: the probability of a component to perform its required functions under stated
conditions for a specified period of time
Unconstrained minimization (1-D)
• Find the minimum of a function
• Derivatives
– at local max or local min, f’(x)=0
– f”(x) > 0 if local min
– f”(x) < 0 if local max
– f”(x) = 0 if saddlepoint
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f(x)
Unconstrained minimization (n-D)
• Optimality conditions
– Necessary condition
– Sufficient condition
f = 0
H is positive definite
(H: Hessian matrix; matrix of 2nd derivatives)
• Gradient based methods
Steepest descent method
Conjugate gradient method
sk  f  xk 
Newton and quasi-Newton methods sk  f  xk   k sk 1
(best known is BFGS)
Hessian
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s k    H k1  f  x k 
Constrained minimization
• Gradient projection methods
 Find good direction tangent to active constraints
 Move a distance and then restore to constraint
boundaries
• Method of feasible directions
 A compromise between objective reduction and
constraint avoidance
• Penalty function methods
• Sequential approximation methods
 Sequential quadratic programming
Iteratively approximate as QP
quadratic
linear
Guaranteed optimum solution
17
Global-Local Optimization
Approaches
Global Methods
Population based
methods
global
local
parameter space
Local Methods
Evolutionary (no history)
Simplex Method (SM)
Genetic Algorithms (GA)
Differential Evolution
Genetic algorithm
Memetic algorithm
Particle swarm
optimization
Ant colony
Harmony search
Gradient Based
Newton’s method (unconstrained)
Steepest descent (unconstrained)
Conjugate gradient (unconstrained)
Sequential Unconstrained
Minimization Techniques (SUMT) (constrained)
Sequential linear programming (constrained)
Sequential quadratic programming (constrained)
Modified Method of Feasible Directions (constrained
Constrained Optimization
Identify :
(1) Design variables (X)
(2) Objective functions to be minimized (F)
(3) Constraints that must be satisfied (g)
Starting point
X
Initial design
Analysis
Analyze the system
F ( X ), g ( X )
Optimizer
Convergence
criteria ?
No
Change design using
Optimization technique
Converge ?
UpdatedX
No
Multiple objective optimization
A set of decision variables that
forms a feasible solution to a
multiple objective optimization
problem is Pareto dominant; if
there exists no other such set
that could improve one decision
variable without making at least
one other decision variable
worse.
Vilfredo Pareto
CONVERGENCE OF PARETO FRONTIER
It is relatively simple to determine an optimal
solution for single objective methods (solution
with the lowest error function)

However, for multiple objectives, we must
evaluate solutions on a “Pareto frontier”

A solution lies on the Pareto frontier when any
further changes to the parameters result in one
or more objectives improving with the other
objective(s) suffering as a result


Once a set of solutions have converged to the
Pareto frontier, further testing is required in order
to determine which candidate force field is
optimal for the problems of interest
Be aware that searches with a limited number of
parameters might “cram” a lot of important
physics into a few parameters
Iteration
Two dimensional objective space
1.4
1.2
1.0
objective 2

Error function
One dimensional objective space
0.8
0.6
0.4
0.2
0.0
converged Pareto surface
0.0
0.2
0.4
0.6
objective 1
0.8
1.0
1.2
Multi-Objective Optimization Demands an
Intelligent Designer
• By definition, a multi-objective design demands
intelligence outside the system based on Pareto
Front
• As the number of parameters and design
variables increase, the objective function
becomes more complex
• As the number of objectives increases, the
number of solutions increases and the objective
function becomes more complex; this leads to
an inordinate amount of options
• As the number of constraints increases, the
number of solutions decreases, but the
complexity increases
Car Part Example: Corvette Cradle
1. Design Parameters
1. Yield Stress
2. Ultimate Strength
3. Energy Absorption
4. Creep Resistance
5. Corrosion Resistance
6. Fatigue Resistance
7. Stiffness
8. Volume/Thickness of Material
2. Constraints
1. Costs
2. Materials Processing Method
3. Time for procurement
Summary
• ID movement should be focused on
engineering and not science
• As the number of variables, design
variables, objectives, and constraints
increase in the design of “something”, then
an intelligence outside the system is
required according to standard design
optimization Pareto arguments
• This argues for a
Creator/Designer/Engineer outside of the
universe