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1 | | -******************************************** |
2 | | -NVIDIA PhysicsNeMo Sym (Latest Release) |
3 | | -******************************************** |
4 | | -.. |
5 | | - TODO: add conf.py and root_doc |
6 | | -
|
7 | | -NVIDIA PhysicsNeMo Sym is a deep learning framework that blends the power of |
8 | | -physics and partial differential equations (PDEs) with AI to build more |
9 | | -robust models for better analysis. |
10 | | - |
11 | | -There is a plethora of ways in which ML/NN models can be applied for |
12 | | -physics-based systems. These can depend based on the availability of |
13 | | -observational data and the extent of understanding of underlying physics. |
14 | | -Based on these aspects, the ML/NN based methodologies can be broadly |
15 | | -classified into forward (physics-driven), data-driven and hybrid approaches |
16 | | -that involve both the physics and data assimilation. |
17 | | - |
18 | | -.. figure:: /images/user_guide/ai_in_computational_sciences_spectrum.png |
19 | | - :alt: AI in computational sciences |
20 | | - :width: 80.0% |
21 | | - :align: center |
22 | | - |
23 | | -With NVIDIA PhysicsNeMo Sym, we aim to provide researchers and industry specialists, |
24 | | -various tools that will help accelerate your development of such models for the |
25 | | -scientific discipline of your need. Experienced users can start with exploring the |
26 | | -PhysicsNeMo Sym APIs and building the models while beginners can use this User Guide |
27 | | -as a portal to explore the possibilities of AI in the domain of scientific |
28 | | -computation. The User Guide comes in with several examples that will help |
29 | | -you jumpstart your development of AI driven models. |
| 1 | +PhysicsNeMo Sym |
| 2 | +=============== |
30 | 3 |
|
31 | 4 | .. toctree:: |
32 | 5 | :maxdepth: 2 |
33 | | - :caption: Learn the Basics |
34 | | - :name: Learn the Basics |
35 | | - :hidden: |
36 | | - |
37 | | - Overview <user_guide/basics/physicsnemo_overview.rst> |
38 | | - Introductory Example <user_guide/basics/lid_driven_cavity_flow.rst> |
39 | | - |
40 | | -.. toctree:: |
41 | | - :maxdepth: 1 |
42 | | - :caption: Theory |
43 | | - :name: Theory |
44 | | - :hidden: |
45 | | - |
46 | | - Physics-Informed Learning <user_guide/theory/phys_informed.rst> |
47 | | - Architectures <user_guide/theory/architectures.rst> |
48 | | - Advanced Schemes <user_guide/theory/advanced_schemes.rst> |
49 | | - Recommended Practices <user_guide/theory/recommended_practices.rst> |
50 | | - Miscellaneous Concepts <user_guide/theory/miscellaneous.rst> |
51 | | - |
52 | | -.. toctree:: |
53 | | - :maxdepth: 1 |
54 | | - :caption: PhysicsNeMo Sym Features |
55 | | - :name: PhysicsNeMo Sym Features |
56 | | - :hidden: |
57 | | - |
58 | | - Geometry and Tesselation Modules <user_guide/features/csg_and_tessellated_module.rst> |
59 | | - Computational Graph, Nodes and Architectures <user_guide/features/nodes.rst> |
60 | | - Constraints <user_guide/features/constraints.rst> |
61 | | - Configuration <user_guide/features/configuration.rst> |
62 | | - Post Processing <user_guide/features/post_processing.rst> |
63 | | - Performance <user_guide/features/performance.rst> |
| 6 | + :caption: Contents |
| 7 | + :name: Contents |
64 | 8 |
|
65 | | -.. toctree:: |
66 | | - :maxdepth: 1 |
67 | | - :caption: Physics-Informed Foundations |
68 | | - :name: Physics-Informed Foundations |
69 | | - :hidden: |
70 | | - |
71 | | - 1D Wave Equation <user_guide/foundational/1d_wave_equation.rst> |
72 | | - 2D Wave Equation <user_guide/foundational/2d_wave_equation.rst> |
73 | | - Spring Mass ODE <user_guide/foundational/ode_spring_mass.rst> |
74 | | - Zero Equation Turbulence <user_guide/foundational/zero_eq_turbulence.rst> |
75 | | - Scalar Transport <user_guide/foundational/scalar_transport.rst> |
76 | | - Linear Elasticity <user_guide/foundational/linear_elasticity.rst> |
77 | | - Inverse Problem <user_guide/foundational/inverse_problem.rst> |
78 | | - |
79 | | -.. toctree:: |
80 | | - :maxdepth: 1 |
81 | | - :caption: Neural Operators |
82 | | - :name: Neural Operators |
83 | | - :hidden: |
84 | | - |
85 | | - Fourier <user_guide/neural_operators/darcy_fno.rst> |
86 | | - Adaptive Fourier <user_guide/neural_operators/darcy_afno.rst> |
87 | | - Physics-Informed <user_guide/neural_operators/darcy_pino.rst> |
88 | | - Deep-O Nets <user_guide/neural_operators/deeponet.rst> |
89 | | - FourCastNet <user_guide/neural_operators/fourcastnet.rst> |
90 | | - |
91 | | -.. toctree:: |
92 | | - :maxdepth: 1 |
93 | | - :caption: Intermediate Case Studies |
94 | | - :name: Intermediate Case Studies |
95 | | - :hidden: |
96 | | - |
97 | | - Variational Examples <user_guide/intermediate/variational_example.rst> |
98 | | - Geometry from STL Files <user_guide/intermediate/adding_stl_files.rst> |
99 | | - Time Window Training <user_guide/intermediate/moving_time_window.rst> |
100 | | - Electromagnetics <user_guide/intermediate/em.rst> |
101 | | - 2D Turbulent Channel <user_guide/intermediate/two_equation_turbulent_channel.rst> |
102 | | - Turbulence Super Resolution <user_guide/intermediate/turbulence_super_resolution.rst> |
103 | | - |
104 | | -.. toctree:: |
105 | | - :maxdepth: 1 |
106 | | - :caption: Advanced Case Studies |
107 | | - :name: Advanced Case Studies |
108 | | - :hidden: |
109 | | - |
110 | | - Conjugate Heat Transfer <user_guide/advanced/conjugate_heat_transfer.rst> |
111 | | - 3D Fin Parameterization <user_guide/advanced/parametrized_simulations.rst> |
112 | | - Heat Transfer with High Conductivity <user_guide/advanced/2d_heat_transfer.rst> |
113 | | - FPGA <user_guide/advanced/fpga.rst> |
114 | | - Industrial Heat Sink <user_guide/advanced/industrial_heat_sink.rst> |
115 | | - |
116 | | - |
117 | | -.. toctree:: |
118 | | - :maxdepth: 2 |
119 | | - :caption: PhysicsNeMo Sym API |
120 | | - :name: PhysicsNeMo Sym API |
121 | | - :hidden: |
122 | | - |
123 | | - api/api_index |
| 9 | + theory_index |
| 10 | + features_index |
| 11 | + examples_index |
| 12 | + api_index |
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