Simulating and understanding phase change | Guest video by Vilas Winstein

3Blue1Brown · Beginner ·📰 AI News & Updates ·8mo ago
Deriving the Boltzmann formula, defining temperature, and simulating liquid/vapor. @SpectralCollective has the second part: https://youtu.be/yEcysu5xZH0 You can play with a simulation of this model here: https://vilas.us/simulations/liquidvapor/ These lessons are funded directly by viewers: https://3b1b.co/support Home page: https://www.3blue1brown.com Notes from Vilas: 1) This open problem is to prove the ergodicity of the deterministic dynamical systems that are used to model the molecule-level physics. A good example of such a dynamical system is the box with particles evolving according to Newton's laws with elastic collisions, like in the video. 2) This video assumes that all probability distributions are discrete, which is the case in the simulations. But one can also set up this formalism for systems with continuous state spaces. Again, a good example is particles in a box, which are not restricted to a lattice, which is only used for visualization purposes in this video. 3) Strictly speaking, these "derivatives" don't make sense since in our simulations the energy can only take on a discrete set of values. But a derivative is the right way to think about this, and is the correct notion in a limiting sense. 4) The factor of -T in the definition of the chemical potential is sort of a historical leftover, but including it has the convenient side effect of allowing the same Boltzmann formula to hold, with an energy function that depends on the chemical potential as well. It should also be noted that temperature must equalize in any situation where the chemical potential equalizes, since it is impossible for systems to exchange molecules without also exchanging energy. 5) This algorithm, where we only choose one pixel at a time, is called Glauber dynamics. There are multiple ways to parallelize it, but the method chosen in this video is to only update each pixel with a low probability at each call of the compute shader, to avoid (frequently) updating two nei
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1 e to the pi i, a nontraditional take (old version)
e to the pi i, a nontraditional take (old version)
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2 Euler's Formula Poem
Euler's Formula Poem
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3 Euler's Formula and Graph Duality
Euler's Formula and Graph Duality
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4 What does it feel like to invent math?
What does it feel like to invent math?
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5 How to count to 1000 on two hands
How to count to 1000 on two hands
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6 Music And Measure Theory
Music And Measure Theory
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7 Fractal charm: Space filling curves
Fractal charm: Space filling curves
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8 The Brachistochrone, with Steven Strogatz
The Brachistochrone, with Steven Strogatz
3Blue1Brown
9 Snell's law proof using springs
Snell's law proof using springs
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10 Triangle of Power
Triangle of Power
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11 Essence of linear algebra preview
Essence of linear algebra preview
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12 Vectors | Chapter 1, Essence of linear algebra
Vectors | Chapter 1, Essence of linear algebra
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13 Linear combinations, span, and basis vectors | Chapter 2, Essence of linear algebra
Linear combinations, span, and basis vectors | Chapter 2, Essence of linear algebra
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14 Linear transformations and matrices | Chapter 3, Essence of linear algebra
Linear transformations and matrices | Chapter 3, Essence of linear algebra
3Blue1Brown
15 Matrix multiplication as composition | Chapter 4, Essence of linear algebra
Matrix multiplication as composition | Chapter 4, Essence of linear algebra
3Blue1Brown
16 Three-dimensional linear transformations | Chapter 5, Essence of linear algebra
Three-dimensional linear transformations | Chapter 5, Essence of linear algebra
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17 The determinant | Chapter 6, Essence of linear algebra
The determinant | Chapter 6, Essence of linear algebra
3Blue1Brown
18 Inverse matrices, column space and null space | Chapter 7, Essence of linear algebra
Inverse matrices, column space and null space | Chapter 7, Essence of linear algebra
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19 Nonsquare matrices as transformations between dimensions | Chapter 8, Essence of linear algebra
Nonsquare matrices as transformations between dimensions | Chapter 8, Essence of linear algebra
3Blue1Brown
20 Dot products and duality | Chapter 9, Essence of linear algebra
Dot products and duality | Chapter 9, Essence of linear algebra
3Blue1Brown
21 Cross products in the light of linear transformations | Chapter 11, Essence of linear algebra
Cross products in the light of linear transformations | Chapter 11, Essence of linear algebra
3Blue1Brown
22 Cross products | Chapter 10, Essence of linear algebra
Cross products | Chapter 10, Essence of linear algebra
3Blue1Brown
23 Change of basis | Chapter 13, Essence of linear algebra
Change of basis | Chapter 13, Essence of linear algebra
3Blue1Brown
24 Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra
Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra
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25 Abstract vector spaces | Chapter 16, Essence of linear algebra
Abstract vector spaces | Chapter 16, Essence of linear algebra
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26 Who cares about topology?   (Old version)
Who cares about topology? (Old version)
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27 3blue1brown channel trailer
3blue1brown channel trailer
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28 Binary, Hanoi and Sierpinski, part 1
Binary, Hanoi and Sierpinski, part 1
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29 Binary, Hanoi, and Sierpinski, part 2
Binary, Hanoi, and Sierpinski, part 2
3Blue1Brown
30 But what is the Riemann zeta function? Visualizing analytic continuation
But what is the Riemann zeta function? Visualizing analytic continuation
3Blue1Brown
31 Tattoos on Math
Tattoos on Math
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32 Fractals are typically not self-similar
Fractals are typically not self-similar
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33 Euler's formula with introductory group theory
Euler's formula with introductory group theory
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34 The essence of calculus
The essence of calculus
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35 The paradox of the derivative | Chapter 2, Essence of calculus
The paradox of the derivative | Chapter 2, Essence of calculus
3Blue1Brown
36 Derivative formulas through geometry | Chapter 3, Essence of calculus
Derivative formulas through geometry | Chapter 3, Essence of calculus
3Blue1Brown
37 Visualizing the chain rule and product rule | Chapter 4, Essence of calculus
Visualizing the chain rule and product rule | Chapter 4, Essence of calculus
3Blue1Brown
38 What's so special about Euler's number e? | Chapter 5, Essence of calculus
What's so special about Euler's number e? | Chapter 5, Essence of calculus
3Blue1Brown
39 Implicit differentiation, what's going on here? | Chapter 6, Essence of calculus
Implicit differentiation, what's going on here? | Chapter 6, Essence of calculus
3Blue1Brown
40 Limits, L'Hôpital's rule, and epsilon delta definitions | Chapter 7, Essence of calculus
Limits, L'Hôpital's rule, and epsilon delta definitions | Chapter 7, Essence of calculus
3Blue1Brown
41 Integration and the fundamental theorem of calculus | Chapter 8, Essence of calculus
Integration and the fundamental theorem of calculus | Chapter 8, Essence of calculus
3Blue1Brown
42 What does area have to do with slope? | Chapter 9, Essence of calculus
What does area have to do with slope? | Chapter 9, Essence of calculus
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43 Higher order derivatives | Chapter 10, Essence of calculus
Higher order derivatives | Chapter 10, Essence of calculus
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44 Taylor series | Chapter 11, Essence of calculus
Taylor series | Chapter 11, Essence of calculus
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45 Pi hiding in prime regularities
Pi hiding in prime regularities
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46 All possible pythagorean triples, visualized
All possible pythagorean triples, visualized
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47 But how does bitcoin actually work?
But how does bitcoin actually work?
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48 How secure is 256 bit security?
How secure is 256 bit security?
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49 Hilbert's Curve: Is infinite math useful?
Hilbert's Curve: Is infinite math useful?
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50 Thinking outside the 10-dimensional box
Thinking outside the 10-dimensional box
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51 Some light quantum mechanics (with minutephysics)
Some light quantum mechanics (with minutephysics)
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52 But what is a neural network? | Deep learning chapter 1
But what is a neural network? | Deep learning chapter 1
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53 Gradient descent, how neural networks learn | Deep Learning Chapter 2
Gradient descent, how neural networks learn | Deep Learning Chapter 2
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54 Backpropagation, intuitively | Deep Learning Chapter 3
Backpropagation, intuitively | Deep Learning Chapter 3
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55 Backpropagation calculus | Deep Learning Chapter 4
Backpropagation calculus | Deep Learning Chapter 4
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56 The hardest problem on the hardest test
The hardest problem on the hardest test
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57 Q&A #2 + Net Neutrality Nuance
Q&A #2 + Net Neutrality Nuance
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58 Why this puzzle is impossible
Why this puzzle is impossible
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59 But what is the Fourier Transform?  A visual introduction.
But what is the Fourier Transform? A visual introduction.
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60 The more general uncertainty principle, regarding Fourier transforms
The more general uncertainty principle, regarding Fourier transforms
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