(Part 2) Best computer simulation books according to redditors
We found 74 Reddit comments discussing the best computer simulation books. We ranked the 34 resulting products by number of redditors who mentioned them. Here are the products ranked 21-40. You can also go back to the previous section.
The assessment is interesting, and from a physical/dynamical perspective, it's very enticing. However I can't help but feel unsatisfied that still it's not clear what society should actually do in such a situation.
I also tend to agree with the wildfire analogy right at the very end and have used it myself a few times. I think the useful thing about a wildfire is its obvious ability to quickly deconstruct a massive amount of space at a molecular level, allowing new life to take its place. Nature, evolution, culture are all emergent properties of hysteresis; the past is encoded deeply into the future. When the environment/constraints of life change quicker than the hysteresis allows, societies (or avalanches) collapse. While catastrophic, these collapses can also open new space for new opportunities to blossom that otherwise would not get the chance to.
So I think the problem is that as humans, a controlled and quick deconstruction is not something we like or are good at doing. Tradition, while useful in it's wisdom, also has an interval of relevance. If the constraints of life change quicker than tradition can explain, one must change and explore the chaos and unknown. The age old dichotomy of left and right or yin and yang. Obviously it's a balance of the two, so that means we need to learn as a society when to be swift, and when to be calm.
In today's world where change seems inevitable and tradition longs for relevancy, we face the dilemma of what we keep and what we throw over board. If we don't figure it out fast enough, the probability of collapse or at least a catastrophe will continue to increase as the constraints of life overpower our ability to make the choices required to create a good future and prevent misery.
PS. The citations on the wiki article on Self-organized Criticality is an interesting place to explore the idea of criticality in nature, the human brain, and society. One of the original authors, Per Bak, wrote a whole book on this subject which I've heard is good though I have not had the chance to read yet.
You need this and pick on of these. Now you just need a shitload of freetime.
My suggestion would be the JCB 3CX or the NH B115.
Anany Levitin's Introduction to the Design and Analysis of Algorithms. The MIT videos are also very useful for self study.
Fun plane. Nice pictures. I've been using it on FlyUK's tours and Highland Connect routes. There is also a pretty helpful guide to flying it on Amazon.
The bible of linear algebra applied to ecology is Hal Caswell's Matrix Population Models. Definitely check this out.
If you want to understand the fundamental concepts of linear algebra (linear functions, vectors, matrices, eigenvalues and eigenvectors), I'm going to indulge in some shameless self-promotion and recommend Chapter 6 of Modeling Life. Go slow (we take about 4 weeks to cover most of that chapter) and do the problems. If you want, you can skip the more technical material on diagonalization, but eigenstuff is important. And if you're at a place that subscribes to SpringerLink, you can download the book free.
Look at the land versus the oceans. Ocean observations are quite sparse, especially back then. There is less confidence with the ocean predictions.
So your conclusion that the ocean model is incomplete is quite true. But it does predict the last 50 years quite well, where we had better data. Land data is much more complete (to a point) and there is higher confidence.
I see your reasoning a lot. It can't reproduce this particular aspect, so it must be all wrong. I can understand it, and see where it's coming from, but in the realm of science, this reasoning doesn't make sense. It means that we've missed something, but we've captured most of it. Most of it means a lot. Most of it means that large portions of it are understood. As for the parts that aren't understood, grants and research focuses on those, on the places of high uncertainty, of incompleteness, of doubt.
If you really want to delve in deep, I'd recommend this website and this book.
:* From "The Discovery of Global Warming, Ocean Currents and Climate"
>Through the 1950s, few scientists found much reason or opportunity to study the slow circulations in the depths. Oceanography was a poorly organized field of research. There were only a few oceanographic institutes, jealously isolated from one another, each dominated by one or a few forceful personalities. The funds for research at sea were wholly inadequate to the vast subject. The economics of shipping and fishing supported only studies of practical interest such as surface currents; little data had been gathered about anything else. The field as a whole scarcely looked like solid science. Theories about ocean circulation had what one expert called "a peculiarly dream-like quality."
I like Anany Levitin's: https://www.amazon.com/Design-Analysis-Algorithms-Anany-Levitin/dp/8120334213
You can also get Lindenmayer's book used on amazon for 20 bucks.
https://www.amazon.com/Algorithmic-Beauty-Plants-Virtual-Laboratory/dp/0387972978/ref=sr_1_fkmrnull_1?keywords=the+algorithmic+beauty+of+plants&qid=1549593472&sr=8-1-fkmrnull
Those books are all quite good, and I would also recommend Jim Albert's Bayesian Computation with R as a supplement to Gelman's Bayesian Data Analysis text, as Albert provides R code for many of Gelman's examples.
I assume googling already got you to the RasterTek tutorials.
Frank D. Luna's book is good, Introduction to 3d Game Programming with DirectX 11. While introductory it's fairly comprehensive of the pipeline and why (sometimes brief, but the main DX docs fill that in).
Beginning DirectX11 Game Programming, is a bit more newcomer friendly but a lot less comprehensive.
HLSL Development Cookbook is a reasonable fill-in for HLSL and shaders that you would actually use. It doesn't cover the nitty gritty like DX api calls, but does explain what you need to have your render states setup as each step of the way.
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If you aren't already familiar with 11 then 12 is likely out of your league for the time being. DX10 began the move towards a lower-level API in comparison to DX9 and OpenGL.
At the risk of self-promotion, I just published a book in this series: A User's Guide to Network Analysis in R.
Books? WHAT YEAR IS IT?
Gotta know your PDE
There's a good introductory textbook on nonlinear dynamical systems and applications to biological systems called Modeling Life that I help teach a class for. It's aimed to be easily digestible for college freshmen so it has some introductory calculus in there but there's also some really nice connections between dynamical systems and real life systems that's outside of what's normally taught in college differential equations courses.