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SciML/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 75%An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks →
- PossiblePossibly related (embedding) · 63%Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte →
- PossiblePossibly related (embedding) · 56%Error-Conditioned Neural Solvers →
- PossiblePossibly related (embedding) · 52%Recovering Governing Equations from Solution Data: Identifiability Bounds for Linear and Nonlinear ODEs →
- PossiblePossibly related (embedding) · 50%Physics-constrained neural networks for surrogate modeling of lossless periodic structures →
- PossiblePossibly related (embedding) · 57%Principled approaches for extending neural architectures to function spaces for operator learning →
- PossiblePossibly related (embedding) · 59%Advances in Neural Controlled Differential Equations →
- PossiblePossibly related (embedding) · 45%x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability →
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)
Implements (incoming)
paperAdvances in Neural Controlled Differential Equationspaperx-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint DecodabilitypaperA Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse ProblemspaperPhysics-Informed Neural Embeddings of PDE Solution FamiliespaperA Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial SystemspaperEntropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical KineticspaperPhysically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology
Related across the graph
paperEntropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical KineticspaperError-Conditioned Neural SolverspaperAdvances in Neural Controlled Differential EquationspaperA Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial SystemspaperPhysics-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 ODEsnewsEnabling local neural operators to perform equation-free system-level analysispaperx-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint DecodabilitypaperA Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse ProblemspaperPhysics-Informed Neural Embeddings of PDE Solution FamiliespaperAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural NetworkspaperPhysics-constrained neural networks for surrogate modeling of lossless periodic structurespaperPhysically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and CosmologynewsPrincipled approaches for extending neural architectures to function spaces for operator learning