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SciML/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Implements
paperAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural NetworkspaperPhysics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with ElectrolytepaperError-Conditioned Neural SolverspaperRecovering Governing Equations from Solution Data: Identifiability Bounds for Linear and Nonlinear ODEspaperPhysics-constrained neural networks for surrogate modeling of lossless periodic structures
Covers (incoming)
Related across the graph
paperError-Conditioned Neural SolverspaperPhysics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with ElectrolytepaperRecovering Governing Equations from Solution Data: Identifiability Bounds for Linear and Nonlinear ODEspaperAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural NetworkspaperPhysics-constrained neural networks for surrogate modeling of lossless periodic structuresnewsPrincipled approaches for extending neural architectures to function spaces for operator learning