probnum.problems¶

Definitions and collection of problems solved by probabilistic numerical methods.

Classes¶

 `TimeSeriesRegressionProblem`(locations, …) Time series regression problem. `InitialValueProblem`(f, t0, tmax, y0[, df, …]) First order ODE initial value problem. `LinearSystem`(A, b[, solution]) Linear system of equations. `QuadratureProblem`(integrand, lower_bd, upper_bd) Numerical computation of an integral.

probnum.problems.zoo¶

Subpackage offering implementations of standard example problems or convenient interfaces to benchmark problems for probabilistic numerical methods. These test problems are meant for rapid experimentation and prototyping.

probnum.problems.zoo.diffeq¶

Test problems involving ordinary differential equations.

Functions¶

 `threebody`([t0, tmax, y0]) Initial value problem (IVP) based on a three-body problem. `vanderpol`([t0, tmax, y0, params]) Initial value problem (IVP) based on the Van der Pol Oscillator, implemented in jax. `rigidbody`([t0, tmax, y0, params]) Initial value problem (IVP) for rigid body dynamics without external forces `threebody_jax`([tmax]) Initial value problem (IVP) based on a three-body problem. `vanderpol_jax`([t0, tmax, y0, params]) Initial value problem (IVP) based on the Van der Pol Oscillator, implemented in jax.

probnum.problems.zoo.filtsmooth¶

Exemplary state space model setups for Bayesian Filtering and Smoothing.

Functions¶

 `benes_daum`([measurement_variance, …]) Filtering/smoothing setup based on the Beneš SDE. `car_tracking`([measurement_variance, …]) Filtering/smoothing setup for a simple car-tracking scenario. `logistic_ode`([y0, timespan, step, params, …]) Filtering/smoothing setup for a probabilistic ODE solver for the logistic ODE. `ornstein_uhlenbeck`([measurement_variance, …]) Filtering/smoothing setup based on an Ornstein Uhlenbeck process. `pendulum`([measurement_variance, timespan, …]) Filtering/smoothing setup for a (noisy) pendulum.

probnum.problems.zoo.linalg¶

Test problems from linear algebra.

Functions¶

 `random_spd_matrix`(dim[, spectrum, random_state]) Random symmetric positive definite matrix. `random_sparse_spd_matrix`(dim, density[, …]) Random sparse symmetric positive definite matrix. `suitesparse_matrix`(name, group[, verbose]) Sparse matrix from the SuiteSparse Matrix Collection.

Classes¶

 `SuiteSparseMatrix`(matid, group, name, nnz, …) SuiteSparse Matrix.