Understanding Randomness in Brownian Matter

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SUMMARY

The discussion centers on the concept of Brownian motion, emphasizing its inherent randomness due to the limitations of computational power and our understanding of quantum mechanics. It asserts that while classical mechanics could theoretically predict particle trajectories with unlimited computing resources and complete initial conditions, such conditions are unattainable. The conversation references "Laplace’s Demon" to illustrate the impossibility of exact predictions in quantum systems, concluding that precise trajectory forecasting of quantum particles is fundamentally unachievable.

PREREQUISITES
  • Understanding of Brownian motion and its implications in physics
  • Familiarity with classical mechanics and its limitations at quantum scales
  • Knowledge of quantum mechanics principles
  • Basic computational theory related to predictive modeling
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  • Research the implications of quantum mechanics on classical physics
  • Explore advanced computational models for simulating Brownian motion
  • Investigate the concept of "Laplace’s Demon" and its relevance to determinism
  • Study the statistical mechanics underlying particle dynamics in fluids
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Physicists, computational scientists, and anyone interested in the intersection of classical and quantum mechanics, particularly in the context of particle dynamics and randomness.

EntropicThinker
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TL;DR
Can Brownian motion be predicted with advanced computing, and what implications does quantum mechanics have on its apparent randomness?
Video on Brownian matter that got me thinking

Brownian motion is a fundamental concept in physics, describing the random movement of particles suspended in a fluid. However, the apparent randomness of this motion is largely due to our limited understanding and computational power. As computational capabilities continue to advance, will it be possible to accurately predict the movement of particles in Brownian motion? If so, would this imply that the motion is deterministic, and what role would quantum mechanics play in our understanding of this phenomenon?
 
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A cubic cm of water weighs about 1 gram.
Water weighs around 18gm/mole.
So there are around 3*1022 molecules of water in a cubic cm.
Computational capabilities are nowhere near this.
 
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It's not just the computational power that is needed. A completely accurate prediction would require complete knowledge of the type, initial position, and initial velocity of every molecule.
(Water molecules move at over 1,000 mph on average in room-temperature water. So every molecule needs to be considered. There are about 1.5 sextillion molecules in a drop of water. So that is a lot of initial data to determine.)
 
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Google for “Laplace’s Demon”.
If classical mechanics applied at arbitrarily small scales, and we had unlimited computing power, and we had exact knowledge of all the initial conditions…. Then yes, we could predict the exact trajectory of every particle in a body of fluid. But we don’t have infinite computing power and even if we did classical mechanics doesn’t apply at sufficiently small scales and quantum mechanics says that there is no such thing as exact knowledge of the classical initial conditions.

So no, we cannot and never will be able to exactly predict the trajectories of quantum particles the way that Laplace was imagining.
 
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Nugatory said:
Google for “Laplace’s Demon”.
If classical mechanics applied at arbitrarily small scales, and we had unlimited computing power, and we had exact knowledge of all the initial conditions…. Then yes, we could predict the exact trajectory of every particle in a body of fluid. But we don’t have infinite computing power and even if we did classical mechanics doesn’t apply at sufficiently small scales and quantum mechanics says that there is no such thing as exact knowledge of the classical initial conditions.

So no, we cannot and never will be able to exactly predict the trajectories of quantum particles the way that Laplace was imagining.
Very insightful , thank you for your answer I appreciate it
 
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