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86

What you're looking for is Landauer's principle. You should be able to find plenty of information about it now that you know its name, but briefly, there is a thermodynamic limit that says you have to use $k_BT \ln 2$ joules of energy (where $k_B$ is Boltzmann's constant and $T$ is the ambient temperature) every time you erase one bit of computer memory. ...


18

I think perhaps some of the other answers are taking computer science to be synonymous with computation. I guess that this is perhaps not what you mean, but rather theoretical computer science. There is obviously a huge overlap with quantum information processing of which I think you are already well aware, so I will ignore that. Much of physics (including ...


17

1) yes, it basically will find a non-optimal solution. At every point, the top of the ray looks for the bigger potential gradient, the charge in the surrounding volume grows, polarizing surrounding material (air, in this case) until a bigger gradient shows up and the ray continues over that direction. This is why the lightining path looks like a jigsaw; its ...


16

Some software I have used or has been recommended to me for physics-related work: WolframAlpha -- when I don't have Maple around, I use it for simple symbolic calculations Maxima -- free open source alternative to Maple/Mathematica Sage -- quite an interesting open source symbolic/numerical package, you can try it online at sagenb.org Scilab/GNU Octave -- ...


12

First off, physics tends to provide a very good background for people who move on to study problems in other areas, which is perhaps why there is a lot of cross-over to computer science. However, there are also a number of areas at the interface of computer science and physics which attract people from both sides: Computer hardware (which is generally ...


10

EDIT: This answer is specifically from the perspective of very computationally oriented fields like theoretical plasma physics. Most physicists can program, and in fact many are rather good programmers. It would be difficult to work in modern physics without being able to program. Unfortunately, many are also not terribly good programmers (I've read many a ...


9

Some info from ASIC world: For example, you processor have 300 mil. transistors, and most of these do some work. But, in order to make for example pure 32-bit add operation you need just about 1000 of them. Others are for caching and passing data back and forth - support functions which are impossible to estimate. So estimations from math side are very hard ...


9

It is probably worth your while to buy Mathematica, Maple, or Matlab, depending on your needs. I wish it weren't so, but this is one area in which the commercial tools are still vastly better than their free counterparts. If you are a student, you can buy these at fairly afforable prices. Maple 14 Student Edition is only $99. Mathematica for Students is ...


8

Assuming a typical computer with CPU processing power ~1 GHz. It means that it can generate output byte sequence at ~$10^9$ byte/s, which is about ~$10^{-13}$ J/K in terms of von Neumann entropy. Also, the power consumption of a typical CPU is ~100 W, which gives entropy ~0.3 J/K at room temperature. So the (minimum ΔS) / (actual ΔS) ~ $10^{-14}$ This ...


7

I've recently discovered Cadabra. A field-theory motivated approach to computer algebra I'm really impressed.


6

I'd like to add that GNU Octave is a very good free alternative to Matlab. Contrary to Scilab which does not aim at being compatible with Matlab, you can practically run your Matlab scripts with Octave with very few modifications (at least with their latest version).


6

This is, no doubt, one of the biggest challenges for realistic simulations: waves crashing, hair moving under wind and whatever other movement involving turbulence will be hard to solve. Though it is true that one can solve the equations of motion for each individual particle in a 'molecular dynamics' fashion, that is just infeasible for a system that goes ...


6

Sage is a Python based system (including Numpy and Scipy) which includes a symbolic computation module. From the Sage homepage: Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface. Mission: Creating a viable free open source ...


6

There's no flaw in your argument. A computer heats the room just as effectively as an electric heater of the same power and you could use the computer to do something useful (Bitcoin mining?) while it's heating your room. There are some practical considerations, though I think these have been sufficiently discussed in the comments. Computers would make for ...


5

Color forces are not like electromagnetic ones. There exist no unbound color carrying particles analogous to the electron, because the forces increase with the distance rather than decrease and collective effects appear only within nuclei through residuals of the colored forces which attract the nucleons and hold them in the nuclei. Collective effects ...


4

I think the main reason why this is so common is that many people who are of the tenured professor age now (50-60) were in graduate school before most colleges offered a Ph.D. in computer science. So back then, people who were interested in theoretical computer science got their doctorate in Mathematics, and people who were interested in applied computer ...


4

From my reasoning and knowledge of one CS professor who has a PhD in astronomy: Above all, the answer depends on your definition of what a "computer scientist" is. What do you mean by "computer scientist"? Someone who does research in a computer science department? Or does perhaps artificial intelligence, algorithm development, or grid computing for a ...


4

Can physical states be treated as information (strings over some alphabet)? There is a distinction between a state and a vector (see this mo question), but disregarding that, we can clearly approximate a vector to any desired precision using a finite-length string. I doubt that anyone can say whether the rounding errors involved grow uncontrollably or ...


3

In computational physics (and other computational disciplines), not enough attention is paid (much of the time) to reproducibility issues and too much is paid to execution speed. The main advantage of "literate" languages like Python is that it is pretty easy to write readable, easily documentable, and testable code, and to do so quickly. That makes it ...


3

I think that Wolfram is arguing that the study of cellular automata and perhaps similar computational systems could serve as an organizational principle, providing a coherent framework to look at different problem (just like the more familiar frameworks provided by physics and chemistry). This explains the title of his new book, A new kind of Science (i.e. ...


3

For all practical purposes today the answers above are very informative. However, as Marek has pointed out above, your fundamental theoretical model of the thermodynamics of computation, on which you are basing the question is, surprisingly, wrong, as we first began to discover 50 years ago (see refs. to Landauer Charlie Bennet, Friedkin, others). ...


3

GiNaC is a c++ symbolic manipulation framework oriented to high-energy physics computations. It has a couple of interactive frontends, although its main usage is as part of the Root framework at CERN. A derivative of GiNaC is Pynac, which forms the backend for symbolic expressions in Sage.


3

For modeling of physical (and chemical) systems on quantum computer even 25-30 qubits would be already quite nice, see Lanyon, et al, “Towards Quantum Chemistry on a Quantum Computer”, Nature Chemistry 2, 106 - 111 (2009) (see also http://arxiv.org/abs/0905.0887 ) Really, quant-ph section in arXiv.org is standard place for papers about quantum computers, ...


3

The acceleration ${\bf a}(t)$ is simply computed from Newton law $F = m a $. It's a function of the forces on the particle, which is (assumedly) computable from the positions ${\bf r}(t)$ (of the entire system, i.e. all particles) at time $t$. This can be seen also in the figures, in the three schemes the acceleration is computed from (and only from) the ...


3

Human power consumption can be guesstimated as 100W, similar to the power consumption of an ordinary computer, plus or minus a few orders of magnitude depending on one's idea of "ordinary". A computer can do billions of flops per second, and it would take me many seconds or minutes to perform one with pen and paper, and furthermore I will make many more ...


3

The article is saying something different altogether. There is a difference between being able to run a program (Turing machine) and being able to decide whether that program evenutally finishes running. The latter is called a $\Pi_1$ decision problem. A classical computer can run any given program, subject to resource limitations, but there is no general ...


2

This is the fecund universe idea, due to Smolin. The original form assumed that a new universe formed every time a black hole appeared (as a sink for the information loss that relativists believed in back then), and then the universe is tuned to maximize the number of black holes formed, constrained by the condition that life is possible. These types of ...


2

The brain is massively parallel, so it tends to come out looking very good. The OP suggested using joules/flop as the measure of (in)efficiency. This leaves considerable ambiguity. I believe the way neurons typically work is that they form something like a weighted average of their binary inputs, and generate a binary output that is based on a threshold ...



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