From 943f75f8b3b9c09eb1b836d27e9f247ee2dc2659 Mon Sep 17 00:00:00 2001 From: Lu Lu Date: Fri, 12 Jul 2019 11:40:21 -0400 Subject: [PATCH] Bump to v0.2.0 --- docs/conf.py | 2 +- docs/index.rst | 13 +++++++------ setup.py | 4 ++-- 3 files changed, 10 insertions(+), 9 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index d05130fb7..0897341e4 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ # The short X.Y version version = "" # The full version, including alpha/beta/rc tags -release = "0.1.1" +release = "0.2.0" # -- General configuration --------------------------------------------------- diff --git a/docs/index.rst b/docs/index.rst index ebdb2acf5..097ddd710 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -13,28 +13,29 @@ Use DeepXDE if you need a deep learning library that - approximates functions from a dataset with/without constraints, - approximates functions from multi-fidelity data. -DeepXDE is extensible to solve other problems in scientific computing. +DeepXDE is extensible to solve other problems in Scientific Machine Learning. + +**DeepXDE Paper**: `arXiv `_ Features -------- DeepXDE supports -- complex domain geometries without tyranny mesh generation. The basic geometries are interval, triangle, rectangle, polygon, disk, cuboid, and sphere. Other geometries can be constructed as constructive solid geometry (CSG) by operations: union, difference, and intersection; +- complex domain geometries without tyranny mesh generation. The primitive geometries are interval, triangle, rectangle, polygon, disk, cuboid, and sphere. Other geometries can be constructed as constructive solid geometry (CSG) using three boolean operations: union, difference, and intersection; - multi-physics, i.e., coupled PDEs; - 4 types of boundary conditions: Dirichlet, Neumann, Robin, and periodic; - time-dependent PDEs are solved as easily as time-independent ones by only adding initial conditions; -- residue-based adaptive training points; +- residual-based adaptive refinement (RAR); - uncertainty quantification using dropout; -- four domain geometries: interval, disk, hyercube and hypersphere; - two types of neural networks: fully connected neural network, and residual neural network; - many different losses, metrics, optimizers, learning rate schedules, initializations, regularizations, etc.; - useful techniques, such as dropout and batch normalization; - callbacks to monitor the internal states and statistics of the model during training; -- compact and nice code, very close to the mathematical formulation. +- enables the user code to be compact, resembling closely the mathematical formulation. All the components of DeepXDE are loosely coupled, and thus DeepXDE is well-structured and highly configurable. -It is easy to add new functions to each modules to satisfy new requirements. +It is easy to customize DeepXDE to meet new demands. User guide ------------ diff --git a/setup.py b/setup.py index 83aff9e57..26f635af5 100644 --- a/setup.py +++ b/setup.py @@ -11,14 +11,14 @@ setup( name="DeepXDE", - version="0.1.1", + version="0.2.0", description="Deep learning library for solving differential equations", long_description=long_description, long_description_content_type="text/markdown", author="Lu Lu", author_email="lululxvi@gmail.com", url="https://github.com/lululxvi/deepxde", - download_url="https://github.com/lululxvi/deepxde/tarball/v0.1.1", + download_url="https://github.com/lululxvi/deepxde/tarball/v0.2.0", license="Apache-2.0", install_requires=install_requires, classifiers=[