copperwire / comp_phys_2 Goto Github PK
View Code? Open in Web Editor NEWLicense: GNU General Public License v3.0
License: GNU General Public License v3.0
This is dependent on #5 being completed
There needs to be implemented a bootstrap or blocking algorithm. We can base this implementation on the code written by Marius that is on the course website. The output should be a variance-reduced local energy.
We also need scripts in python to do comparative analysis in particular for the task 1.d (importance sampling).
Currently the energy goes to a very large number, I think I just flipped the signs wrong on the jastrow factor so I'll update when next I work on the project
I think this class could possibly inherit from GausianNonInterNumeric
but I'll have to give it a think or two
See title. In nqs
In the report we should include the derivations of the local energy for the trial wavefunctions in 1-3 dimensions with and without interaction for 1 and N particles.
We also have to have a derivation of the drift force.
For elliptical traps the formulation for the external potential is given as equation 1 in the project description.
No implementations has this correctly implemented
So far computation of the interactive term has been done rather naively with updating the entire correlation term for each new position. This is wildly inefficient. Since each term in the correlation function is dependent only on the particle positions in question we can either:
Suggestion 1
requires little to no change in implementation - as only the proposed sampling R_p
needs be changed by two lines. Suggestion 2
requires a bit more thought as we don't have an implementation of taking indices to the evaluation of the local energy. It might be simplest to just pass dummy variables to all methods, i.e the call to psi_t -> E_l(R, i, j)
for all wavefunctions and hamiltonians, but the indices are in effect dummy variables for the non-interactive case.
See title
There should be a standardized output of the run-metrics and output of the local energies according to some rule.
The run-metrics should be calculated in the monte_carlo
function of the VMC
class.
I think the metrics we should record are:
1e3
cycles1e3
stepsThe output should be to a file specified by a string
class member of VMC
that is set with the method set_params
And the filename should contain meta-information about the run (that the file might need to contain?) i.e. number of particles, numeric or analytic solution, if there is interaction etc.
When running the current code the local energy does not stabilize to an equilibrium value (i.e. it changes for each run). I suspect that the problem lies in the computation of the metropolis hastings test.
I propose that we follow this sign convention for all MH implementations, let:
then the MH test should read:
for a random uniformly distributed number epsilon in the interval [0, 1].
EDIT: WRONG MH TEST - SIGN SHOULD BE REVERSED AND NEW POSITION SHOULD BE IN THE DIVISOR
See title. In nqs
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.