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Package {PINNProgCens}


Type: Package
Title: Physics-Informed Neural Networks for Progressive Censoring
Version: 0.1.0
Description: Implementation of Physics-Informed Neural Networks ('PINN') for lifetime estimation under progressive Type-II censoring schemes. Combines parametric baseline hazards with physical differential degradation models.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: deSolve, stats
Config/roxygen2/version: 8.0.0
NeedsCompilation: no
Packaged: 2026-08-05 07:02:56 UTC; Dr. O. J. Obulezi
Author: Okechukwu J. Obulezi [aut, cre]
Maintainer: Okechukwu J. Obulezi <oj.obulezi@unizik.edu.ng>
Repository: CRAN
Date/Publication: 2026-08-09 08:10:15 UTC

Physics Residuals at Collocation Points

Description

Physics Residuals at Collocation Points

Usage

compute_ode_residuals(sol_colloc, z_colloc, phi, ode_func)

Arguments

sol_colloc

Solved ODE states across collocation grid.

z_colloc

Collocation evaluation grid points.

phi

Physics differential parameters.

ode_func

Differential system function.

Value

Vector of physical model residuals.


Default Physical ODE System (Degradation Process)

Description

Default Physical ODE System (Degradation Process)

Usage

default_ode_system(t, h, phi)

Arguments

t

Time variable.

h

Current degradation state.

phi

Parameter vector governing the differential equation.

Value

List containing derivative dh/dt.


Maximum Likelihood Estimation via PINN Optimization

Description

Maximum Likelihood Estimation via PINN Optimization

Usage

fit_pinn_progcens_mle(
  times,
  R,
  z_colloc,
  ode_func = default_ode_system,
  max_epochs = 50
)

Arguments

times

Observed progressive censored failure times.

R

Progressive removal counts.

z_colloc

Collocation points for differential equation loss.

ode_func

Differential system function.

max_epochs

Maximum optimization iterations.

Value

List containing parameter estimates and standard errors.

Examples

times_obs <- c(0.85, 1.42, 2.10)
R_vec     <- c(1, 0, 1)
colloc    <- seq(0.1, 3.0, length.out = 5)
fit_pinn_progcens_mle(times_obs, R_vec, colloc, max_epochs = 5)

Interpolate Trajectory State

Description

Interpolate Trajectory State

Usage

interpolate_state(tau, sol_obs)

Arguments

tau

Intermediary integration time.

sol_obs

Trajectory solution matrix.

Value

Scalar estimated state value.


Physics-Regularized Hazard Rate Mapping

Description

Physics-Regularized Hazard Rate Mapping

Usage

pinn_hazard(t, alpha, beta, h_t, psi)

Arguments

t

Evaluation time point.

alpha

Weibull scale parameter.

beta

Weibull shape parameter.

h_t

Degradation state at time t.

psi

List containing network weights W2 and bias b2.

Value

Hazard rate value.

Examples

pinn_hazard(1.0, 1.0, 1.0, 0.5, list(W2 = matrix(1,1,1), b2 = 0))

Combined Structural Loss (Censored Likelihood + Physics Residual)

Description

Combined Structural Loss (Censored Likelihood + Physics Residual)

Usage

pinn_progcens_loss(params, times, R, z_colloc, ode_func, mu = 0.1)

Arguments

params

List of current candidate parameters (alpha, beta, phi, psi).

times

Vector of observed failure times.

R

Vector of progressive censorship removals.

z_colloc

Vector of physics collocation evaluation points.

ode_func

Physics ODE differential system function.

mu

Physics loss regularization hyperparameter penalty weight.

Value

Total scalar PINN structural loss value.


Softplus Activation Function

Description

Softplus Activation Function

Usage

softplus(x)

Arguments

x

Numeric vector or matrix input.

Value

Smooth approximation to ReLU.

Examples

softplus(c(-1, 0, 1))

Forward State Trajectory Integration

Description

Forward State Trajectory Integration

Usage

solve_ode_trajectory(times, phi, ode_func)

Arguments

times

Vector of evaluation time points.

phi

Differential parameters.

ode_func

Right-hand-side differential equation function.

Value

Matrix of interpolated trajectories at evaluated times.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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