PINA and PINO_Applications
PINA is a general-purpose physics-informed neural network framework, while PINO_Applications is a specialized collection of use cases demonstrating the application of Physics-Informed Neural Operators (PINOs), making them complementary tools where the latter showcases advanced techniques built on concepts related to the former's domain.
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 PINO_Applications
shawnrosofsky/PINO_Applications
Applications of PINOs
This project helps scientists and engineers accurately predict the behavior of complex physical systems governed by partial differential equations (PDEs), such as wave propagation or fluid dynamics. It takes existing simulation data for various physical phenomena and outputs highly accurate predictions of system states, even for higher resolutions or scenarios not explicitly seen during initial training. Researchers and computational scientists who need to model complex physical systems with high fidelity would find this useful.
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