Many-Body Numeric Integration Algorithm

In summary, the conversation discusses numeric methods for solving non-separable partial differential equations, specifically in the context of coupled cluster analysis in nuclear and chemical systems. The proposed method involves creating a system of equations and performing numeric integration on each body in the system with other coordinates held constant. For sparse systems, Pre-conditioned Conjugate Gradient or Multigrid methods may be used, but for non-sparse and non-linear systems, the best approach is unclear.
  • #1
Enjolras1789
52
2
That is to say, how does one go about it for a non-separable partial differential equation?

Let me preface by saying that I am not asking for an answer of perturbation theory, variational theory, mean-field theory, or some sort of self-consistent guess-and-check method (i.e., coupled cluster). I know and acknowledge that coupled cluster is king of the hill for numeric methods of analysis for nuclear and chemical systems. My question is geared more toward the pure math of it...at the risk of possibly showing ignorance of the math behind using coupled cluster.

Here is what I think is what works, please tell me if this is on the money.

Normal methods of numeric quadrature are just fine for all of the ODE terms, it is the non-separable terms that represent a bit more of a challenge. Thus, one creates a system of equations, one for each body. One does numeric integration on the first body in the system with all other coordinates not of that body held constant, then the second equation of the system is numerically integrated on the second set of variables, etc., until one has a matrix which has as many rows as bodies and as many columns as terms in the Hamiltonian.

Perhaps in more explicit terms...if I am in 3 dimensions, and if I have 3 bodies in question, letting the bodies be 1,2,3 with variables x1,y1,z1; x2,y2,z2; x3,y3z3...
I will take the non-separable PDE, begin numeric integration with x2,y2,z2 and x3,y3,z3 held constant, generating the first matrix equation, then the next two matrix equations will be the PDE numerically integrated with first x1,y1,z1 and x3,y3,z3 constant, then x1,y1,z1 x2,y2,z2 held constant?

Thanks for your time,
 
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  • #2
For a sparse system of linear equations I think it's using
Pre-conditioned Conjugate Gradient or Multigrid methods.
For non-sparse and non-linear systems then I'm not sure.
J.D.
 

FAQ: Many-Body Numeric Integration Algorithm

What is a Many-Body Numeric Integration Algorithm?

A Many-Body Numeric Integration Algorithm is a computational method used to solve systems of equations that involve the interactions between multiple bodies. It is commonly used in physics and astronomy to model the movements and behaviors of particles, such as planets in a solar system.

How does a Many-Body Numeric Integration Algorithm work?

The algorithm uses numerical integration techniques to approximate the solutions to the equations of motion for each body in the system. This involves breaking down the system into smaller time intervals and using iterative calculations to determine the positions and velocities of the bodies at each time step.

What are the advantages of using a Many-Body Numeric Integration Algorithm?

One of the main advantages is its ability to accurately model complex systems with multiple interacting bodies. It is also highly efficient and can handle large amounts of data, making it useful for simulations and predictions.

Are there any limitations to the Many-Body Numeric Integration Algorithm?

One limitation is that it assumes the bodies in the system are point masses, meaning they have no physical size or shape. This may not accurately reflect the real-world behavior of objects with physical dimensions. Additionally, the algorithm can become unstable if the time intervals used are too large.

How is a Many-Body Numeric Integration Algorithm used in research?

The algorithm is commonly used in research to study the movements and interactions of particles in various scientific fields, including physics, astronomy, and chemistry. It can also be used to model complex systems in engineering and biology.

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