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Changelog

Source: NEWS.md

pinnr 0.1.1

  • Fixed intermittent NaN or wrong losses with precision = "float64". Coordinate and data tensors were built from one-column R matrices, which torch_tensor() does not copy for double precision, so they could point to memory that R had already freed. pinnr now always copies R data into tensors. The failure was seen most often on Apple Silicon Macs.
  • README: Intel Macs on macOS 14 or older need torch 0.16.3 or earlier.

pinnr 0.1.0

  • First release.

  • pinn_fit() no longer throws away the model when the loss becomes NaN or infinite. It keeps the weights with the lowest loss seen so far, restores them, and returns the fit with a warning and diverged = TRUE.

  • Output columns in data with no observed values are now dropped instead of making the data loss NaN. This could happen with windows, where each window sees only its own slice of the data.

  • plot(fit, type = "solution") draws 1D solutions on a dense grid, so ODE curves are smooth even when times has few points.

  • Window fits no longer store a copy of the whole problem in their call.

  • pinn_sample() (and therefore conditions) uses every combination of the fixed values when several variables are fixed, e.g. the four corners for where = list(x = "both", y = "both"), and gives every fixed value at least one point.

  • Argument checks reject NA in hidden and infinite counts and rates with clear messages.

  • pinn_ode() builds problems from deSolve-style ODE models, with exact initial conditions by default.

  • pinn_problem() and pinn_domain() define PDE problems on rectangular domains; residuals are R functions with arguments matched by name and the derivative operator d().

  • Conditions: pinn_dirichlet(), pinn_neumann(), pinn_initial() and pinn_condition(), plus output transforms for hard constraints.

  • Parameter estimation from partly observed, noisy data through unknown and data.

  • pinn_fit() trains with Adam and optional L-BFGS, Latin hypercube sampling, resampling, time marching (windows) for long ODE horizons, GPU devices and single or double precision.

  • Methods predict(), residuals(), coef(), plot(), summary(), and pinn_save() / pinn_load().