FEA and Darwinian Computer Simulations

_{Gil Dodgen

June 11, 2011

Intelligent Design}

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In my work as a software engineer in aerospace R&D I use what is arguably the most sophisticated, universally applicable, finite-element analysis program ever devised by the most brilliant people in field, refined and tested for 35 years since its inception in the mid-1970s for the development of variable-yield nuclear weapons at Lawrence Livermore National Laboratory. It is called LS-DYNA (LS for Livermore Software, and DYNA for the evaluation of dynamic, nonlinear, transient systems).

A finite element is an attempt to descretize on a macro level what occurs at a molecular level in a physical system, so that a result is amenable to a practical computational solution. The learning curve for the use of this sophisticated technology is extremely steep, and the most important thing one learns is that empirical verification of the simulation results is absolutely required to validate the predictions of any FEA model.

In an LS-DYNA simulation, all the laws of physics and the mathematics that describe them are precisely known. In addition, all of the material properties associated with the physical objects are precisely quantified with empirical verification (density, modulus of elasticity, and much more).

The FEA solver computes a physical result by solving millions of differential equations with a minimal integration time step based on the time required for a disturbance traveling at the speed of sound to traverse the smallest finite element with the greatest mass density.

Even with all of this, and countless man-years of experience by sophisticated and experienced users (LS-DYNA has been used for many years in the auto industry for simulating car crashes) empirical verification is always required, by actually crashing a car to validate the FEA results.

In light of all this, consider the typical Darwinian computer simulation and the trust that could be put in one.

Darwinian computer simulations are simply a pathetic joke as they relate to biological reality. This should be obvious to anyone with experience in the field of legitimate computer simulation.

Comments

I don't quite see why modeling the Ph of something would help if, for example you were testing an hypothesis about population dynamics - unless of course there was a specific reason for Ph to be included, for example if you were looking at the effect of ocean acidification on fish populations. The implied argument seems to be that models of an aspect of evolution are only valid if they account for every atom. If so then I would disagree, and agree with Dr Liddle that a model only needs to include aspects relevant to the hypothesis being tested.DrBot_{June 12, 2011
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Actually, I guess my point is the same as DrBot's, but I would still appreciated clarification.Elizabeth Liddle_{June 12, 2011
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Gil: Like Neil Rickert, I don't see the connection between the first, very interesting part of your OP, and your comments regarding "Darwinian computer simulations". I'm not even sure whether you are referring to simulations designed to demonstrate the principles of Darwinian evolution, or evolutionary algorithms designed to solve real world problems. Either way, the fact that more sophisticated software exists doesn't seem to me to be relevant to the validity of "Darwinian computer simulations". What is relevant to their validity of course, is the hypothesis they are designed to test, or the problem they are designed to solve. Could you clarify?Elizabeth Liddle_{June 12, 2011
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DrBot, As part of any realistic GA model you would have to provide numerous parameters (pH, temperature, chemical environment...) which would make the model unbearably complicated. And how would you test this in the lab? And if you were to simplify the model by making various assumptions, how would it then represent reality?NZer_{June 12, 2011
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NeilBJ, Fascinating question. As far as I am aware, quantum mechanical computations are so complex that even supercomputers can only model simple molecules. I suspect Finite element analysis as mentioned by Gil would be insufficient to cope with biological complexity.NZer_{June 12, 2011
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If there are no algorithms that model actual biological systems and if not enough is yet known to write a true emulation, then you are correct. Current evolutionary algorithms are a joke.
Evolutionary algorithms are used for, amongst other things, logic circuit design. It is certainly true that not enough is known to write a complete emulation of biological evolution but of course the same is true of most things that science is studying that is why they are called simulations and why they are useful (and not a joke) because the differences between the simulation and reality help you understand reality better - they help direct the science. Not enough is known to write a complete emulation of the weather but weather simulations are not 'just a joke', they are very useful. There are plenty of genetic algorithms that model actual biological systems though, they just do so to different levels of abstraction depending on the aspect of biology being studied. Other genetic algorithms are not attempts to model biology, they are being used for other purposes, including design.DrBot_{June 12, 2011
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I am a retired logic design engineer. I designed the logic for VLSI chips that became part of a computer. Every one of my designs had to pass running in an emulation program. The emulation program modeled every feature of an actual chip: capacitance, inductance, signal delays, power consumption, and of course the logic gates. In theory the operation of the emulation program would not be distinguishable from the operation of an actual chip. In practice, the logic delays in the emulation program would be different from the actual chip, but the delays nevertheless would be known. (I hope I have remembered the details correctly. It’s been 20 plus years since I worked on this stuff. I became an application programmer in meantime.) As I understand evolutionary algorithms, they would be classified as simulations and not emulations. In other words they are loose models of the evolutionary process and not exact representations. A couple of questions come to mind. Are there any evolutionary algorithms that model actual biological systems? I am thinking of Avida, which evolves logic circuits, so it obviously does not model an actual biological system. Do evolutionary biologists yet know enough about evolutionary processes so that they could write an emulation based on a real biological system? If there are no algorithms that model actual biological systems and if not enough is yet known to write a true emulation, then you are correct. Current evolutionary algorithms are a joke.NeilBJ_{June 11, 2011
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But..but..METHINSKITISLIKEAWEASEL provides all the proof one could ever need!!!Matteo_{June 11, 2011
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Personally, I am undecided on the significance of evolution simulations. However, I am wondering why you think your experience with FEA is relevant. As far as I know, that's addressing a very different kind of problem.Neil Rickert_{June 11, 2011
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