Overview
ADCME is suitable for conducting inverse modeling in scientific computing. The purpose of the package is to: (1) provide differentiable programming framework for scientific computing based on TensorFlow automatic differentiation (AD) backend; (2) adapt syntax to facilitate implementing scientific computing, particularly for numerical PDE discretization schemes; (3) supply missing functionalities in the backend (TensorFlow) that are important for engineering, such as sparse linear algebra, constrained optimization, etc. Applications include
full wavelength inversion
constitutive modeling in solid mechanics
learning hidden geophysical dynamics
physics based machine learning
parameter estimation in stochastic processes
The package inherents the scalability and efficiency from the well-optimized backend TensorFlow. Meanwhile, it provides access to incooperate existing C/C++ codes via the custom operators. For example, some functionalities for sparse matrices are implemented in this way and serve as extendable "plugins" for ADCME.
Read more about the methodology, the philosophy, the insights and the perspective about ADCME: slides. Start with tutorial to solve your own inverse modeling problems.
Installation
It is recommended to install ADCME via
using Pkg; Pkg.add("ADCME")Make sure to run the ADCME doctor to check the health of your ADCME installation.
using ADCME
doctor()However, in some cases, you may want to install the package and configure the environment manually.
Step 1: Install ADCME on a computer with Internet access and zip all files from the following paths
julia> using Pkg
julia> Pkg.depots()The files will contain all the dependencies.
Step 2: Build ADCME mannually.
using Pkg;
ENV["manual"] = 1
Pkg.build("ADCME")However, in this case you are responsible for configuring the environment by modifying the file
using ADCME;
print(joinpath(splitdir(pathof(ADCME))[1], "deps/deps.jl"))Quick Overview
Let's consider a simple problem: we want to solve the unconstrained optimization problem
where $x_i\in [-10,10]$ and $n=100$.
We solve the problem using the L-BFGS-B method.
using ADCME
n = 100
x = Variable(rand(n)) # Use `Variable` to mark the quantity that gets updated in optimization
f = sum(100((x[2:end]-x[1:end-1])^2 + (1-x[1:end-1])^2)) # Use typical Julia syntax
sess = Session(); init(sess) # Create and initialize a session is mandatory for activating the computational graph
BFGS!(sess, f, var_to_bounds = Dict(x=>[-10.,10.]))To get the value of $\mathbf{x}$, we use run to extract the values
run(sess, x)The above command will return a value close to the optimal values $\mathbf{x} = [1\ 1\ \ldots\ 1]$.
Contributing
Contribution and suggestions are always welcome. In addition, we are also looking for research collaborations. You can submit issues for suggestions, questions, bugs, and feature requests, or submit pull requests to contribute directly. You can also contact the authors for research collaboration.
Video Lectures and Slides
Here is a collection of video lectures and slides related to ADCME.