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How nature approaches its goal and what we can learn from it

· 2 min read

For the past 4 years, I have been busy understanding how numerical simulations of physical phenomena works. Surprisingly, most of these simulations share the concept of minimizing energy. It turns out the nature is efficient (or lazy!) when performing tasks such as moving or deforming. What I want to discuss here is the actual steps that are used for the algorithms behind these simulations and what we can learn from them to approach our goals.

A typical algorithm starts with:

  1. Define an objective that you want to minimize, for example, potential energy.
  2. Start with an initial guess of the expected minimum point.
  3. Progress with small steps toward the objective. Meanwhile, check if you are heading toward the minimum.
  4. In practice, sometimes there are multiple minimum points (just choose one) or there is no one (change the objective or the initial guess), or it is hard to reach the minimum point (then stick with what you got from the progress).

What can be inspired by that process:

  1. If you want to do something define your goal first.
  2. Start doing it, just start somewhere!.
  3. Progress towards your goal with small steps and don't forget that what you do should align with your goal.
  4. Sometimes there are multiple ways to achieve your goal (choose the easiest way), or you can't reach your goal (then change your goal or approach), or sometimes you can't perfect your goal (then stick with what you got, good enough is enough).