The Bible’s Perspective on Intelligent Design

1. 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 ChristianGod.[n 2] Wikipedia

2. 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

3. Questions • What is science? • What is engineering? • What is design? • What is innovation? • What are the differences between the above?

4. 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)

5. InventorEngineer Scientist Rocket Scientist

6. Science and Engineering Clearly Defined “Science seeks to understand what is; Engineering seeks to create what never was.” —Theodore von Kármán

7. 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

8. Engineering • Engineering requires “design” and “science” to “innovate” and then organizing, procuring, and producing the product

9. 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

10. 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

11. 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

12. 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

13. N w t N 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 p………………..parameters of the problem Find d………………..the design variables of the problem Min f(d,p)…………...objective function Subject to 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 Given w, N Find t Min t (i.e., weight) S.t. σ – σcrit = N/(w t) – σY ≤0 tL≤ t ≤ tU

14. 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

15. f(x) 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

16. Hessian Unconstrained minimization (n-D) • Optimality conditions • Necessary condition f = 0 • Sufficient condition H is positive definite (H: Hessian matrix; matrix of 2nd derivatives) • Gradient based methods Steepest descent method Conjugate gradient method Newton and quasi-Newton methods (best known is BFGS)

17. quadratic linear Guaranteed optimum solution 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

18. Global-Local Optimization Approaches • Global Methods • Population based methods • Genetic algorithm • Memetic algorithm • Particle swarm optimization • Ant colony • Harmony search global local parameter space Local Methods • 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) • Evolutionary (no history) Simplex Method (SM) Genetic Algorithms (GA) Differential Evolution

19. Constrained Optimization • Identify : • Design variables (X) • Objective functions to be minimized (F) • Constraints that must be satisfied (g) Starting point Initial design Analysis Analyze the system Optimizer Convergence criteria ? Converge ? Updated No No Change design using Optimization technique

20. 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

21. Convergence of pareto frontier One dimensional objective space • 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 Error function Iteration Two dimensional objective space converged Pareto surface

22. 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 variablesincrease, the objective function becomes more complex • As the number of objectivesincreases, the number of solutionsincreases and the objective function becomes more complex; this leads to an inordinate amount of options • As the number of constraintsincreases, the number of solutionsdecreases, but the complexity increases

23. Car Part Example: Corvette Cradle • Design Parameters • Yield Stress • Ultimate Strength • Energy Absorption • Creep Resistance • Corrosion Resistance • Fatigue Resistance • Stiffness • Volume/Thickness of Material • Constraints • Costs • Materials Processing Method • Time for procurement

24. 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