PINA and XPINNs
PINA provides a general-purpose framework for physics-informed neural networks, while XPINNs extends this approach with domain decomposition techniques for solving large-scale nonlinear PDEs, making them complements that can be used together for different problem scales and complexities.
About PINA
mathLab/PINA
Physics-Informed Neural networks for Advanced modeling
This tool helps scientists and engineers build predictive models that adhere to known physical laws or integrate with existing data. You input your scientific problem, including any governing equations or experimental data, and it outputs a trained neural network that can simulate complex systems or make predictions while respecting physics. It's designed for researchers, computational scientists, and anyone working with scientific data and simulations.
About XPINNs
AmeyaJagtap/XPINNs
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
When solving complex physics problems, this framework helps researchers and engineers model systems governed by nonlinear partial differential equations (PDEs), even those with intricate geometries or discontinuous behaviors. It takes your PDE problem definition and produces a trained neural network model that can predict system behavior more efficiently than standard methods. This tool is ideal for computational scientists, physicists, and engineers working on simulations and analyses where traditional PDE solvers struggle with complexity or computational cost.
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