Input File Generator
Fill in the parameters below to generate a ready-to-run Python input script for TENSO. The preview updates in real time, and warnings will appear if the chosen parameters may cause excessive memory usage or long runtimes.
Simulation Method
Choose how the open-system dynamics are propagated. The form below adapts to the method you select.
Bath Parameters
Each component contributes one bath mode. Spectral density: $J_\textrm{DL}(\omega) = \frac{2\lambda}{\pi}\frac{\omega_c\,\omega}{\omega^2+\omega_c^2}$
Each component contributes two bath modes. Spectral density: $J_\textrm{BO}(\omega) = \frac{4\lambda}{\pi}\frac{\eta\,\omega_0^2\,\omega}{(\omega^2-\omega_0^2)^2+4\eta^2\omega^2}$
System Parameters
$H_S$ — real or complex entries, units cm⁻¹
Each coupling is an $M\times M$ operator $Q_n$ that couples the system to its own (independent) copy of the bath above. Add more than one for multi-bath / non-commuting fluctuations (e.g. one local projector per site).
$|\psi\rangle$ — $M$ complex amplitudes (need not be normalized; conventions follow the basis order).
Time-Dependent Drive
The drive adds a time-dependent term to the system Hamiltonian: $H(t) = H_S + f(t)\,V$, entering the dynamics as $-\tfrac{i}{\hbar}\,f(t)\,[V,\,\cdot\,]$. You define the scalar field $f(t)$ (returned in cm⁻¹, with $t$ in fs) and the operator $V$ it couples to.
$f(t) = A\,\exp\!\left[-\tfrac{1}{2}\left(\tfrac{t-t_0}{\sigma}\right)^2\right]\cos(\omega t + \varphi)$, with $\sigma = \mathrm{FWHM}/(2\sqrt{2\ln 2})$ and $\omega$ obtained from the carrier frequency.
$V$ — the $M\times M$ operator the field couples to (default $\sigma_x$ for a two-level system).
Solver & Tensor Network
1e-5 to 1e-7.Propagation Settings
{fname}.dat.log (density matrix) and {fname}.debug.log.Advanced Settings
Expert controls. Each is emitted into the generated script only when you change it from its default, so the output stays clean. Options that don't apply to the chosen method are hidden.
train is lean on memory; trees balance depth.