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GitHub / kochlisGit / Physics-Informed-Neural-Network-PINN-Tensorflow

Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equations.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kochlisGit%2FPhysics-Informed-Neural-Network-PINN-Tensorflow

Stars: 2
Forks: 3
Open issues: 0

License: None
Language: Jupyter Notebook
Size: 467 KB
Dependencies parsed at: Pending

Created at: about 3 years ago
Updated at: about 2 years ago
Pushed at: about 3 years ago
Last synced at: about 2 years ago

Topics: automatic-differentiation, boundary-conditions, calculus, deep-learning, gradients, hessian-matrix, initial-conditions, jacobian-matrix, machine-learning, mathematics, neural-network, optimization, ordinary-differential-equations, partial-differential-equations, physics, physics-informed-neural-networks, pinn, python, simulations-physics, tensorflow

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