Two-Fluid-1D Configuration Reference

The tf-1d solver evolves a 1D electrostatic two-fluid (electron + ion) system closed with a kinetic dispersion table and an optional particle-trapping model:

∂n/∂t + ∂x(n u) = 0
∂u/∂t + u ∂x u = −(∂x p)/n − (q/m) E + ν_L[u, E, δ]
∂p/∂t + u ∂x p + γ p ∂x u = −2 n u (q/m) E
∂δ/∂t = −v_φ ∂x δ + Γ(E) |E| / (1 + δ²)

with p ≡ P/m the mass-normalized pressure. The field is obtained spectrally from the charge density, E_k = i (q_i n_{i,k} + q_e n_{e,k}) / k.

The Landau damping term ν_L is interpolated from a complex-frequency table (see adept.electrostatic.get_complex_frequency_table) and may be reduced locally by the trapping variable δ, whose growth rate Γ comes from the optional learned closure (see models).

Example configs live in configs/tf-1d/.

Top-level keys

Key	Required	Description
solver	yes	Must be tf-1d.
mlflow	yes	Experiment name and run name.
units	yes	Plasma normalization.
grid	yes	Spatial and temporal grid.
save	yes	Output / diagnostic save points.
physics	yes	Per-species fluid and closure parameters.
drivers	yes	External electrostatic drivers.
models	no	Learned-closure (neural network) specification.
adjoint	no	Adjoint method hint. Currently unused by the solver.

mlflow

mlflow:
  experiment: tf1d-epw-test
  run: test

units

units:
  normalizing_temperature: 2000eV
  normalizing_density: 1.5e21/cc

Sets n0, T0, v0 = √(T0/m_e), ωp0, and x0 = v0/ωp0. Lengths in the config are in Debye lengths and times in 1/ωp0.

grid

grid:
  nx: 16
  xmin: 0.0
  xmax: 20.94
  tmin: 0.0
  tmax: 500.0

Field	Type	Description
nx	int	Number of spatial cells (periodic, spectral derivatives).
xmin, xmax	float	Box extent in Debye lengths.
tmin, tmax	float	Simulation time window in 1/ωp0.

dx, dt = 0.05 dx, and nt are derived at runtime; tmax is snapped to a whole number of steps and the step count is capped at 1e6.

save

save:
  t:
    tmin: 0.5
    tmax: 500.0
    nt: 1000
  x:
    xmin: 0.0
    xmax: 20.94
    nx: 16
  kx:            # optional
    kxmin: 0.0
    kxmax: 0.3
    nkx: 2

Block	Required	Fields	Description
t	yes	tmin, tmax, nt	Save times. All three are required — tf-1d does not fall back to the grid values.
x	yes	xmin, xmax, nx	Real-space interpolation grid for the saved fields.
kx	no	kxmin, kxmax, nkx	k-space diagnostic. Omit the block entirely to skip it.

physics

Both ion and electron blocks are required, even when a species is frozen with is_on: false.

physics:
  electron:
    is_on: true
    landau_damping: true
    mass: 1.0
    charge: -1.0
    T0: 1.0
    gamma: kinetic
    trapping:
      is_on: true
      model: zk
      kld: 0.3
      nuee: 1.0e-7
      nn: 8|8
  ion:
    is_on: false
    landau_damping: false
    mass: 1836.0
    charge: 1.0
    T0: 1.0
    gamma: 3
    trapping:
      is_on: false
      kld: 0.3
      nuee: 1.0e-9

Field	Type	Default	Description
is_on	bool	—	Evolve this species. When false, dn/dt = du/dt = dP/dt = 0.
landau_damping	bool	—	Apply the tabulated Landau damping rate ω_i(k) to the momentum equation.
mass	float	—	Species mass normalized to m_e.
charge	float	—	Species charge normalized to e (electrons are -1.0).
T0	float	—	Initial temperature normalized to the reference T0.
gamma	int | float | str	—	Adiabatic index. The literal string kinetic uses the kinetic EPW dispersion table for the restoring force instead of a fixed γ.
trapping	block	—	Particle trapping model, see below.

physics.<species>.trapping

Field	Type	Default	Description
is_on	bool	—	Evolve the trapping variable δ and modify the damping term.
kld	float	—	kλ_D at which the trapping model is evaluated (sets v_φ and the model normalization).
model	none | zk | delta	none	Damping-reduction model. Only read when is_on is true. delta scales the damping by 1/(1 + δ²); zk uses the Zakharov–Karpman collision frequency (work in progress); none leaves the damping unmodified.
nuee	float	null	Electron–electron collision frequency normalized to ωp0. Required in practice when is_on is true — it sets the trapping growth-rate normalization.
nn	str	null	Legacy neural-network shape hint (e.g. `8

Note

trapping.is_on: true with no model key falls back to none, meaning δ is evolved but does not feed back on the damping rate. Set model explicitly if you want trapping to modify the physics.

Warning

trapping.is_on: true is currently not runnable. ParticleTrapper reads the growth-rate network from args["nu_g"], but nothing in the live code path builds it from the models block, so the solve fails with KeyError: 'nu_g' on the first step. Only trapping.is_on: false configs run end to end today.

drivers

drivers:
  ex:
    "0":
      k0: 0.3
      w0: 1.1598
      dw0: 0.0
      t_c: 80
      t_w: 100
      t_r: 20
      x_c: 600
      x_w: 800
      x_r: 80
      a0: 4.e-3

drivers.ex is a dictionary of independently-enveloped electrostatic drivers keyed by a string index; their contributions are summed.

Field	Type	Description
k0	float	Wavenumber in 1/λ_D.
w0	float	Angular frequency in ωp0.
dw0	float	Frequency offset added to w0.
t_c, t_w, t_r	float	Temporal envelope center, width, and rise/fall length.
x_c, x_w, x_r	float	Spatial envelope center, width, and rise/fall length. Use a very large x_w for a spatially uniform drive.
a0	float	Driver amplitude. The applied field is scaled by `

models

Optional learned-closure specification used by the trapping growth rate nu_g (and, when implemented, a damping model nu_d). Set models: false to disable learned closures entirely.

Warning

This block is currently accepted and validated but not consumed — the live solver never instantiates these networks. See the warning under trapping.

models:
  file: models/weights.eqx     # or false for untrained weights
  nu_g:
    in_size: 3
    out_size: 1
    width_size: 8
    depth: 4
    activation: tanh
    final_activation: tanh

Field	Type	Default	Description
file	str | bool	false	Path to serialized equinox weights, or false to start untrained.
nu_g	block	null	equinox.nn.MLP spec for the trapping growth rate.
nu_d	block	null	equinox.nn.MLP spec for a learned damping rate.

Each MLP block takes in_size, out_size, width_size, depth, activation, and an optional final_activation.