Build physics residual point sets (S1 – S4)

Generates four sets of collocation points (random soil-property vectors and associated pF values) used to evaluate the physics constraints during training.

Usage

build_residual_sets(
  df_raw,
  x_inputs,
  S1 = 1500L,
  S2 = 500L,
  S3 = 500L,
  S4 = 1500L,
  pF_lin_min = 5,
  pF_lin_max = 7.6,
  pF0_pos = 6.2,
  pF1_neg = 7.6,
  pF_sat_min = -2,
  pF_sat_max = -0.3,
  seed = 123L
)

Arguments

df_raw

Data frame with covariate columns (training split).

x_inputs

Character vector of covariate column names.

S1

Number of S1 points — dry-end linearity (default 1500).

S2

Number of S2 points — non-negativity at pF0 (default 500).

S3

Number of S3 points — non-positivity at pF1 (default 500).

S4

Number of S4 points — saturated plateau (default 1500).

pF_lin_min

Lower pF for the S1 linearity constraint (default 5.0).

pF_lin_max

Upper pF for the S1 linearity constraint (default 7.6).

pF0_pos

pF at which theta must be >= 0 — S2 (default 6.2).

pF1_neg

pF at which theta must be <= 0 — S3 (default 7.6).

pF_sat_min

Lower pF for the S4 plateau constraint (default -2.0).

pF_sat_max

Upper pF for the S4 plateau constraint (default -0.3).

seed

Integer random seed (default 123).

Value

A named list with four data frames: set1, set2, set3, set4. Each data frame has one row per collocation point, with columns corresponding to x_inputs (sampled uniformly within training range) and a pF column.

Examples

if (FALSE) { # \dontrun{
df <- data.frame(
  clay       = c(20, 30, 10),
  silt       = c(30, 40, 20),
  sand_total = c(50, 30, 70),
  Depth_num  = c(15, 30, 60)
)
sets <- build_residual_sets(df, c("clay", "silt", "sand_total", "Depth_num"),
                            S1 = 50L, S2 = 20L, S3 = 20L, S4 = 50L)
} # }