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Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
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# Self-Adaptive PINN - Official Implementation ## Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism [AAAI-MLPS 2021] ### Levi McClenny<sup>1,2</sup>, Ulisses Braga-Neto<sup>1</sup> ### Accepted to [AAAI-MLPS 2021](https://sites.google.com/view/aaai-mlps/proceedings?authuser=0) ### Update: The self-adaptive implementations of the Allen-Cahn, Burgers, and Helmholtz PDE systems, shown here, are available in our new package [*TensorDiffEq*](https://github.com/tensordiffeq/TensorDiffEq) #### Those examples in *TensorDiffEq* are available [here](https://github.com/tensordiffeq/TensorDiffEq/tree/main/examples) Paper: https://arxiv.org/pdf/2009.04544.pdf Abstract: _Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, the original PINN algorithm is known to suffer from stability and accuracy problems in cases where the solution has sharp spatio-temporal transitions. These stiff PDEs require an unreasonably large number of collocation points to be solved accurately. It has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of stiff PDEs. To accomplish this, previous approaches have used fixed weights hard-coded over regions of the solution deemed to be important. In this paper, we propose a fundamentally new method to train PINNs adaptively, where the adaptation weights are fully trainable, so the neural network learns by itself which regions of the solution are difficult and is forced to focus on them, which is reminiscent of soft multiplicative-mask attention mechanism used in computer vision. The basic idea behind these Self-Adaptive PINNs is to make the weights increase where the corresponding loss is higher, which is accomplished by training the network to simultaneously minimize the losses and maximize the weights, i.e., to find a saddle point in the cost surface. We show that this is formally equivalent to solving a PDE-constrained optimization problem using a penalty-based method, though in a way where the monotonically-nondecreasing penalty coefficients are trainable. Numerical experiments with an Allen-Cahn stiff PDE, the Self-Adaptive PINN outperformed other state-of-the-art PINN algorithms in L2 error by a wide margin, while using a smaller number of training epochs. An Appendix contains additional results with Burger's and Helmholtz PDEs, which confirmed the trends observed in the Allen-Cahn experiments._ <sub><sub><sup>1</sup>Texas A&M Dept. of Electrical Engineering, College Station, TX</sub></sub><br> <sub><sub><sup>2</sup>US Army CCDC Army Research Lab, Aberdeen Proving Ground/Adelphi, MD</sub></sub><br> ## Requirements Code was implemented in `python 3.7` with the following package versions: ``` tensorflow version = 2.3 keras version = 2.2.4 ``` and `matplotlib 3.1.1` was used for visualization. It is expected that any combination of recent numpy/matplotlib will be sufficient, however issues have been experienced on tensorflow versions <2.3.0 ### Virtual Environment (Optional) **(Mac)** To create a virtual environment to run this code, download the repository either via `git clone` or by clicking download at the top of github, then navigate to the top-level folder in a terminal window and execute the commands ``` python3 -m venv --system-site-packages ./venv source ./venv/bin/activate ``` This will create a virtual environment named `venv` in that directory (first line) and drop you into it (second line). At that point you can install/uninstall package versions without effecting your overall environment. You can verify you're in the virtual environment if you see `(venv)` at the beginning of your terminal line. At this point you can install the exact versions of the packages listed here with the pip into the venv: ``` pip install tensorflow==2.3 n
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