BPINN solver Docs(Manual and tutorial) #781
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docs/src/manual/bpinns.md
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| # `BayesianPINN` Discretizer for PDESystems | |||
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| Using the Bayesian PINNs solvers, we can solve general nonlinear PDEs,ODEs and Also simultaniously perform PDE,ODE parameter Estimation. | |||
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| Using the Bayesian PINNs solvers, we can solve general nonlinear PDEs,ODEs and Also simultaniously perform PDE,ODE parameter Estimation. | |
| Using the Bayesian PINNs solvers, we can solve general nonlinear PDEs, ODEs and also simultaneously perform parameter estimation on them. |
docs/src/manual/bpinns.md
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| Using the Bayesian PINNs solvers, we can solve general nonlinear PDEs,ODEs and Also simultaniously perform PDE,ODE parameter Estimation. | ||
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| Note: The BPINN PDE solver also works for ODEs defined using ModelingToolkit, [ModelingToolkit.jl PDESystem documentation](https://docs.sciml.ai/ModelingToolkit/stable/systems/PDESystem/). Despite this the ODE specific BPINN solver `BNNODE` [refer](https://docs.sciml.ai/NeuralPDE/dev/manual/ode/#NeuralPDE.BNNODE) exists and uses `NeuralPDE.advancedhmc_pinn_ode` at a lower level. |
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| Note: The BPINN PDE solver also works for ODEs defined using ModelingToolkit, [ModelingToolkit.jl PDESystem documentation](https://docs.sciml.ai/ModelingToolkit/stable/systems/PDESystem/). Despite this the ODE specific BPINN solver `BNNODE` [refer](https://docs.sciml.ai/NeuralPDE/dev/manual/ode/#NeuralPDE.BNNODE) exists and uses `NeuralPDE.advancedhmc_pinn_ode` at a lower level. | |
| Note: The BPINN PDE solver also works for ODEs defined using ModelingToolkit, [ModelingToolkit.jl PDESystem documentation](https://docs.sciml.ai/ModelingToolkit/stable/systems/PDESystem/). Despite this, the ODE specific BPINN solver `BNNODE` [refer](https://docs.sciml.ai/NeuralPDE/dev/manual/ode/#NeuralPDE.BNNODE) exists and uses `NeuralPDE.advancedhmc_pinn_ode` at a lower level. |
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| # Using `ahmc_bayesian_pinn_pde` with the `BayesianPINN` Discretizer for the Kuramoto–Sivashinsky equation | |||
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Is this ahmc_bayesian_pinn_pde or advancedhmc_pinn_pde? Because I see advancedhmc_pinn_pde in the manuals page.
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| # Using `ahmc_bayesian_pinn_pde` with the `BayesianPINN` Discretizer for the Kuramoto–Sivashinsky equation | |
| # Using Bayesian PINNs for Uncertainty-Aware solutions on the Kuramoto–Sivashinsky equation |
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Why would this be low level? This is just a bpinn tutorial
docs/src/tutorials/low_level_2.md
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| # Add noise to dataset | ||
| datasetpde[1][:, 1] = datasetpde[1][:, 1] .+ randn(size(datasetpde[1][:, 1])) .* 5 / 100 .* | ||
| datasetpde[1][:, 1] | ||
| plot!(datasetpde[1][:, 2], datasetpde[1][:, 1]) |
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Can you separate below code into a separate code block? This would allow plots to be displayed.
src/PDE_BPINN.jl
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| # AdvancedHMC.jl is still developing convenience structs so might need changes on new releases. | ||
| * `Kernel`: Choice of MCMC Sampling Algorithm object with appropriat args/kwargs (AdvancedHMC.jl implemenations HMC/NUTS/HMCDA). | ||
| * `Adaptorkwargs`: A NamedTuple containing the chosen Adaptor, it's Metric and targetacceptancerate, as follows : | ||
| * `Adaptor`: https://turinglang.org/AdvancedHMC.jl/stable/ |
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In lines like this, can you explain it in a line and if possible link documentation specific to it instead of the repo?
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| # Investigating `symbolic_discretize` with the 1-D Burgers' Equation | |||
| # Investigating `symbolic_discretize` with the `PhysicsInformedNN` Discretizer for the 1-D Burgers' Equation | |||
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That's kind of a given since non-Bayesian is standard, I don't think the extra phrase is needed.
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| # Investigating `symbolic_discretize` with the `PhysicsInformedNN` Discretizer for the 1-D Burgers' Equation | |
| # Investigating `symbolic_discretize` with the 1-D Burgers' Equation |
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okay, ill add this change in a seperate PR.
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Checklist
contributor guidelines, in particular the SciML Style Guide and
COLPRAC.
Additional context
closes issue #779