Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914685358440448 |
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| author | Maczuga, Paweł Sikora, Maciej Skoczeń, Maciej Rożnawski, Przemysław Tłuszcz, Filip Szubert, Marcin Łoś, Marcin Dzwinel, Witold Pingali, Keshav Paszyński, Maciej |
| author_facet | Maczuga, Paweł Sikora, Maciej Skoczeń, Maciej Rożnawski, Przemysław Tłuszcz, Filip Szubert, Marcin Łoś, Marcin Dzwinel, Witold Pingali, Keshav Paszyński, Maciej |
| contents | We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1) it is compatible with Google Colab which allows automatic execution on cloud environment; (2) it supports two dimensional time-dependent PDEs; (3) it provides simple interface for definition of the residual loss, boundary condition and initial loss, together with their weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it allows for customizing the number of layers and neurons per layer, as well as for arbitrary activation function; (6) the learning rate and number of epochs are available as parameters; (7) it automatically differentiates PINN with respect to spatial and temporal variables; (8) it provides routines for plotting the convergence (with running average), initial conditions learnt, 2D and 3D snapshots from the simulation and movies (9) it includes a library of problems: (a) non-stationary heat transfer; (b) wave equation modeling a tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor growth simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03755 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab Maczuga, Paweł Sikora, Maciej Skoczeń, Maciej Rożnawski, Przemysław Tłuszcz, Filip Szubert, Marcin Łoś, Marcin Dzwinel, Witold Pingali, Keshav Paszyński, Maciej Computational Engineering, Finance, and Science Machine Learning Mathematical Software Numerical Analysis G.1.8; G.1.10; J.2; J.3; G.4; I.6.4; I.m We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1) it is compatible with Google Colab which allows automatic execution on cloud environment; (2) it supports two dimensional time-dependent PDEs; (3) it provides simple interface for definition of the residual loss, boundary condition and initial loss, together with their weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it allows for customizing the number of layers and neurons per layer, as well as for arbitrary activation function; (6) the learning rate and number of epochs are available as parameters; (7) it automatically differentiates PINN with respect to spatial and temporal variables; (8) it provides routines for plotting the convergence (with running average), initial conditions learnt, 2D and 3D snapshots from the simulation and movies (9) it includes a library of problems: (a) non-stationary heat transfer; (b) wave equation modeling a tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor growth simulations. |
| title | Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab |
| topic | Computational Engineering, Finance, and Science Machine Learning Mathematical Software Numerical Analysis G.1.8; G.1.10; J.2; J.3; G.4; I.6.4; I.m |
| url | https://arxiv.org/abs/2310.03755 |