Comments (2)
KirillZubov
commented on August 23, 2024
Goal list for the project
- general ODE solver
- simple general PDE solver for one dim and first-order task
- prototype of interpretation interface for generalized PDE interfaceModelingToolkit PDESystems
- example for second-order equation
- support two-dimensionality
- combine into one solver
- support AD
- scattered points
- support high-order AD
from neuralpde.jl.
ChrisRackauckas
commented on August 23, 2024
The biggest portion of such a project for us would be to make it automatic. The way to do this would be to integrate with the ModelingToolkit.jl PDESystem setup, showcased here:
SciML/DifferentialEquations.jl#469
Basically it would need to plug into this system and then automatically generate the right neural networks and loss functions from the specification of the PDE.
from neuralpde.jl.
Related Issues (20)
- Problem in replicating INVERSE PROBELM tutorial on ODE HOT 23
- Parameter Estimation in NNODE
- Parameter Estimation formulation in PINNs HOT 5
- Documentation for BayesianPINN and ahmc_bayesian_pinn_pde
- Encountering Precompilation Error Messages When Adding NeuralPDE Package in Julia HOT 3
- Distance functions as a description of complex geometries HOT 1
- Bump CUDA Compat HOT 1
- How can I solve complex-valued ordinary differential equations (ODEs) using neural networks, given limitations with complex data types in libraries like Lux? HOT 7
- Can't get the @named pde_system to work with a ode system HOT 4
- Add support for equations with complex numbers HOT 20
- Neural Adapter Documentation Example Not Working HOT 6
- DGM Tutorial fails HOT 2
- KeyError when solving Parameter Identification problem using NeuralPDE.jl HOT 9
- Linear parabolic example throws an error on new julia install HOT 2
- NeuralPDE slows when using old weights and biases on a new but related ode HOT 3
- NeuralPDE with LuxAMDGPU, Optimize.solve throws error
- GPU Compatibility Issue: Compilation Error with Complex-Valued Data in LuxCUDA Broadcasting Kernel HOT 8
- Tutorial: imposing constraint on PINN solution of Fokker Planck fails HOT 5
- The trial solutions to additional_loss function are different depending on parameters
- API PINO PDE HOT 5
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