Technology • Open Source
Our research involves analyzing measured data and understanding them through simulations. Some codes written for these purposes are shared on GitHub with the community.
If you find the code useful, please consider to star the repository on GitHub.
#1 : Universal rank-order method
Highly vectorized python implementation of a universal rank-order method to analyze noisy data. The method is presented in the paper Phys Rev. X 9, 031039 (arXiv version) and is valuable for
- ● nonlinear regression with heavy-tailed noise, and for
- ● distinguishing between deterministic chaos and stochastic noise.
#2 : Quantum transport simulations in 1D
Transport is a Python package to simulate quantum transport of one-dimensional systems. The package can numerically solve quantum scattering problems at real or complex potentials for particles that impinge from either side (lead). The results are transmission and reflection amplitudes and scattering wave functions.
Highly vectorized python implementation of a universal rank-order method to analyze noisy data. The method is presented in the paper Phys Rev. X 9, 031039 (arXiv version) and is valuable for
- ● nonlinear regression with heavy-tailed noise, and for
- ● distinguishing between deterministic chaos and stochastic noise.
Transport is a Python package to simulate quantum transport of one-dimensional systems. The package can numerically solve quantum scattering problems at real or complex potentials for particles that impinge from either side (lead). The results are transmission and reflection amplitudes and scattering wave functions.
