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Sathvik Bhagavan
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docs: fix rendering of Bayesian PINN docstrings
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src/BPINN_ode.jl

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@@ -123,18 +123,19 @@ function BNNODE(chain, Kernel = HMC; strategy = nothing, draw_samples = 2000,
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end
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"""
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Contains ahmc_bayesian_pinn_ode() function output:
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1> a MCMCChains.jl chain object for sampled parameters
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2> The set of all sampled parameters
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3> statistics like:
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> n_steps
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> acceptance_rate
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> log_density
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> hamiltonian_energy
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> hamiltonian_energy_error
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> numerical_error
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> step_size
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> nom_step_size
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Contains `ahmc_bayesian_pinn_ode()` function output:
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1. A MCMCChains.jl chain object for sampled parameters.
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2. The set of all sampled parameters.
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3. Statistics like:
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- n_steps
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- acceptance_rate
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- log_density
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- hamiltonian_energy
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- hamiltonian_energy_error
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- numerical_error
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- step_size
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- nom_step_size
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"""
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struct BPINNstats{MC, S, ST}
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mcmc_chain::MC
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end
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"""
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BPINN Solution contains the original solution from AdvancedHMC.jl sampling(BPINNstats contains fields related to that)
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> ensemblesol is the Probabilistic Estimate(MonteCarloMeasurements.jl Particles type) of Ensemble solution from All Neural Network's(made using all sampled parameters) output's.
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> estimated_nn_params - Probabilistic Estimate of NN params from sampled weights,biases
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> estimated_de_params - Probabilistic Estimate of DE params from sampled unknown DE parameters
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BPINN Solution contains the original solution from AdvancedHMC.jl sampling (BPINNstats contains fields related to that).
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1. `ensemblesol` is the Probabilistic Estimate (MonteCarloMeasurements.jl Particles type) of Ensemble solution from All Neural Network's (made using all sampled parameters) output's.
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2. `estimated_nn_params` - Probabilistic Estimate of NN params from sampled weights, biases.
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3. `estimated_de_params` - Probabilistic Estimate of DE params from sampled unknown DE parameters.
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"""
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struct BPINNsolution{O <: BPINNstats, E, NP, OP, P}
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original::O

src/advancedHMC_MCMC.jl

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@@ -344,8 +344,8 @@ end
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!!! warn
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Note that ahmc_bayesian_pinn_ode() only supports ODEs which are written in the out-of-place form, i.e.
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`du = f(u,p,t)`, and not `f(du,u,p,t)`. If not declared out-of-place, then the ahmc_bayesian_pinn_ode()
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Note that `ahmc_bayesian_pinn_ode()` only supports ODEs which are written in the out-of-place form, i.e.
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`du = f(u,p,t)`, and not `f(du,u,p,t)`. If not declared out-of-place, then the `ahmc_bayesian_pinn_ode()`
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will exit with an error.
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## Example

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