Changelog

Inverse Problem PYthon library

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https://hippylib.github.io

Version 2.3.0, released on Sept 6, 2019

  • Reimplement BiLaplacianPrior and MollifiedBiLaplacianPrior using a more general framework SqrtPrecisionPDE_Prior, which also supports Gaussian vector fields.
  • Update the prior distribution in model_subsurf.py and tutorial/3_SubsurfaceBayesian.ipynb to use Robin boundary conditions to alleviate boundary artifacts.
  • Update the data misfit term in model_ad_diff.py and tutorial/4_AdvectionDiffusionBayesian.ipynb to use discrete observations in space and time.

Version 2.2.1, released on March 28, 2019

  • Bug fix missing mpi_comm in TimeDependentVector
  • Bug fix in the initialization of the global variable parRandom

Version 2.2.0, released on Dec 12, 2018

  • Add new class GaussianRealPrior that implements a finite-dimensional Gaussian prior
  • Add a callback user-defined function that can be called at the end of each inexact Newton CG iteration
  • Add a __version__ and version_info attribute to hIPPYlib
  • Add setup.py to (optionally) install hIPPYlib via pip
  • Add deprecation mechanism
  • Deprecate TimeDependentVector.copy(other) in favor of TimeDependentVector.copy() for consistency with dolfin.Vector.copy
  • Deprecate _BilaplacianR.inner(x,y) for consistency with dolfin.Matrix
  • CI enhancement via build matrix

Version 2.1.1, released on Oct 23, 2018

  • Update README.md and paper according to JOSS reviewers’s comments
  • Add contributing guidelines
  • Fix some typos in notebooks (thanks to Christian Boehm)

Version 2.1.0, released on July 18, 2018

  • Alleviate boundary artifacts (inflation of marginal variance) in Bilaplacian-like priors using Robin boundary conditions
  • Allow the user to select different matplotlib colormaps in jupyter notebooks
  • Buxfix in the acceptance ratio of the gpCN MCMC proposal

Version 2.0.0, released on June 15, 2018

  • Introduce capabilities for non-Gaussian Bayesian inference using Mark Chain Monte Carlo methods. Kernels: mMALA, pCN, gpCN, IS. Note: API subject to change
  • Support domain-decomposition parallelization (new parallel random number generator, and new randomized eigensolvers)
  • The parameter, usually labeled a, throughout the library, has been renamed to m, for model parameter. Interface changes:
    • PDEProblem.eval_da –> PDEProblem.evalGradientParameter
    • Model.applyWua –> Model.applyWum
    • Model.applyWau –> Model.applyWmu
    • Model.applyRaa –> Model.applyWmm
    • gda_tolerance –> gdm_tolerance in the parameter list for Newton and QuasiNewton optimizers
    • gn_approx –> gass_newton_approx as parameter in function to compute Hessian/linearization point in classes Model, PDEProblem, Misfit, Qoi, ReducedQoi
  • Organize hippylib in subpackages
  • Add sphinx documentation (thanks to E. Khattatov and I. Ambartsumyan)

Version 1.6.0, released on May 16, 2018

  • Bugfix in PDEVariationalProblem.solveIncremental for non self-adjoint models
  • Add new estimator for the trace and diagonal of the prior covariance using randomized eigendecomposition
  • In all examples and tutorial, use enviromental variable HIPPYLIB_BASE_DIR (if defined) to add hIPPYlib to PYTHONPATH

Version 1.5.0, released on Jan 24, 2018

  • Add support for FEniCS 2017.2

Version 1.4.0, released on Nov 8, 2017

  • Add support for Python 3
  • Enchantments in PDEVariationalProblem: it now supports multiple Dirichlet condition and vectorial/mixed function spaces
  • Bugfix: Set the correct number of global rows, when targets points fall outside the computational domain
  • More extensive testing with Travis Integration

Version 1.3.0, released on June 28, 2017

  • Improve hashdist installation support
  • Switch license to GPL-2
  • Add support for FEniCS 2017.1

Version 1.2.0, released on April 24, 2017

  • Update instruction to build FEniCS: hashdist and docker
  • Update notebook to nbformat 4
  • Let FEniCS 2016.2 be the preferred version of FEniCS
  • Add Travis integration

Version 1.1.0, released on Nov 28, 2016

  • Add partial support for FEniCS 2016.1 (Applications and Tutorial)
  • Improve performance of the randomized eigensolvers

Version 1.0.2, released on Sep 30, 2016

  • Use vector2Function to safely convert dolfin.Vector to dolfin.Function
  • Optimize the PDEVariationalProblem to exploit the case when the forward problem is linear
  • Update notebook 1_FEniCS101.ipynb

Version 1.0.1, released on Aug 25, 2016

  • Add support in hippylib.Model and hippylib.Misfit for misfit functional with explicit dependence on the parameter

Version 1.0.0, released on Aug 8, 2016