NeuralPDE.jl
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SciML
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
This commit adds energy loss functions. These are loss functions analogous to those obtained from PDE equations or boundary equations, but the energy integrand can be given explicitly in symbolic...
I have been confused about the parameter estimation of differential equation parameters here for a while now. I think the current formulation of the problem is suboptimal. The loss function...
Hi, I noticed that in some cases, importing NeuralPDE.jl before Interpolations.jl can lead to an error. ### MWE ```julia using Pkg Pkg.activate(mktempdir()) Pkg.add([(; name = "Interpolations", version = "v0.14.7"), (;...
Such that `makedocs` `warnonly = [:missing_docs, :example_block]` can be removed.
Periodic boundary conditions is not working. When I run the following code ```julia using NeuralPDE, Lux, ModelingToolkit, Optimization, OptimizationOptimisers, Random, Plots import ModelingToolkit: Interval @parameters x,y,t @variables u(..) L=1 tmax=1...
If you have 2 or more functions as Neural nets, say f and g. And they are coupled by a differential equation. If we already idea how f should look...
Minor thing, but this was playing around with the two sets of introductory code: 1) https://github.com/SciML/NeuralPDE.jl/blob/master/README.md#example-solving-2d-poisson-equation-via-physics-informed-neural-networks 2) https://docs.sciml.ai/NeuralPDE/stable/tutorials/gpu/ Essentially, naively extending the example [1] in the readme with the push...
refer [Paper](https://www.sciencedirect.com/science/article/pii/S0021999123004370#kws0010)
