Metadata-Version: 2.1
Name: arby
Version: 0.1
Summary: UNKNOWN
Home-page: https://gitlab.com/aaronuv/arby
Author: Aarón Villanueva
Author-email: aaron.villanueva@unc.edu.ar
License: UNKNOWN
Description: # Arby
        
        [Model Order Reduction (MOR)](https://en.wikipedia.org/wiki/Model_order_reduction)
        is a technique for reducing the computational complexity of mathematical models in
        numerical simulations.
        
        Arby is a fully data-driven Python module to construct reduced bases,
        empirical interpolants and surrogate models from training data.
        
        # Install
        
            pip install arby
        
        # Quick Usage
        
        Suppose we want to build a surrogate model for a family of real functions $f_\lambda(x)$
        parametrized by a real number $\lambda\in[\lambda_{min},\lambda_{max}]$ and $x\in[a,b]$.
        We have discretizations of both domains in, say, 101 and 1001 points respectively,
        ```
        lambda_params = np.linspace(lambda_min, lambda_max, 101)
        x_samples = np.linspace(a, b, 1001)
        ```
        The next step is to build a training set of functions associated to the discretizations.
        
        ```
        training_data = [f(lambda, x_samples) for lambda in lambda_params]
        ```
        This is an array of shape $(101,1001)$.
        
        Then we can build a surrogate model with `arby` using:
        
            from arby import ReducedOrderModeling as ROM
            f_model = ROM(training_space=training_data,
                          physical_interval=x_samples,
                          parameter_interval=lambda_params)
            
        With our `f_model` we can get function samples for any parameter $\lambda$ in the
        interval $\lambda\in[\lambda_{min},\lambda_{max}]$.
        
            new_param = 0.554
            f_model_new_param = f_model.surrogate(new_param)
            plt.plot(x_samples, model_new_param)
            plt.show()
        
        # License
        
        MIT
        
        ***
        
        (c) 2020 Aaron Villanueva
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
