[andrewfromx]

You should start with a Monte Carlo implementation using the Metropolis algorithm.

1. Create a grid with random up/down spins

2. Add random magnetic fields at each point

3. Pick points randomly on the grid

4. Calculate energy changes if you were to flip a spin

5. Decide whether to accept the flip based on temperature and energy differences

https://github.com/tjacovich/Ising_Model

https://github.com/prtkm/ising-monte-carlo/blob/master/ising...

[sks38317]

Thanks so much—this really helps me get a better sense of how to approach it. Starting from the Ising model with Monte Carlo makes a lot of sense now that you’ve explained it this way. I’ll go through the repos you shared and try implementing it step-by-step. Really appreciate you taking the time to break it down!

[andrewfromx]

sure but all I did was use these prompts:

"I'm working on a small system simulation project that explores how instability can emerge from the interaction of various variables over time. Recently, I’ve been trying to incorporate the RFIM (Random Field Ising Model) into the logic—but I’ve hit a wall. It’s not the math or the concept itself, but rather the total lack of Korean-language resources or beginner-friendly guides that’s been challenging. any well-documented codebases I could learn from or could point me toward approachable explanations or tutorials"

"put this into simpler english: You should start with a Monte Carlo implementation using the Metropolis algorithm. You now create a lattice with random initial spins... make random fields for each site... randomly selecting sites. Then calculating energy changes for potential spin flips. Accepting/rejecting changes based on energy differences and temperature"

[sks38317]

Thanks again for your explanation. Just to clarify—what I understood is that the Monte Carlo method (using something like the Metropolis algorithm) is useful because it introduces probabilistic decision-making into the system. That helps prevent extreme or unrealistic outcomes, since not every spin flips deterministically.

Also, by having each spin affected by neighboring spins and external random fields, it naturally simulates interaction between variables. So it reflects both local dependencies and external noise, which is exactly the kind of behavior I want in my simulation.

I had been trying to account for interactions between variables, but I ran into a lot of frustration due to extreme or unstable outputs. Thanks to your guidance, though, I was finally able to break through that wall— and I’ve started thinking of ways to restructure and improve the model accordingly.

Let me know if I misunderstood anything.

Thanks a lot.

[andrewfromx]

I guess the point I'm making is why are you not just asking AI these questions?

------------ This probabilistic approach allows the system to:

Escape local energy minima (preventing the system from getting stuck in unrealistic configurations)

Properly sample the thermodynamic equilibrium states according to their Boltzmann weights

Model thermal fluctuations realistically

The beauty of the RFIM specifically is its balance between:

Ordered tendencies (through the J parameter controlling spin-spin interactions)

Disorder and frustration (through the random fields)

Thermal noise (through the temperature parameter)

This combination creates the complex behaviors you're looking for in your simulation - like phase transitions, hysteresis, and avalanche effects. The random fields introduce "frustration" into the system, where different forces compete and create rich, emergent behaviors. --------

that's all from ai