RTS Smoother-Guided Learning of Physics-Based Neural Differential Models
Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the dynamics are unknown and only a subset of the state variables are measured. We propose a hybrid neural--physics framework in which the known components of the ODE are kept explicit and the missing components are represented by a neural network. The proposed method consists of two stages where we alternate between state and parameter estimation and iterate until a predetermined criterion is met. Specifically, in the f
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- PossiblePossibly related (embedding) · 54%Principled approaches for extending neural architectures to function spaces for operator learning →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “RTS Smoother-Guided Learning of Physics-Based Neural Differe” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Ahmet Demirkaya →
“RTS Smoother-Guided Learning of Physics-Based Neural Differential Models”
- LinkedLinked via arxiv author · 85%Georgios Stratis →
“RTS Smoother-Guided Learning of Physics-Based Neural Differential Models”
- LinkedLinked via arxiv author · 85%Tales Imbiriba →
“RTS Smoother-Guided Learning of Physics-Based Neural Differential Models”
- LinkedLinked via arxiv author · 85%Zachary D. Danziger →
“RTS Smoother-Guided Learning of Physics-Based Neural Differential Models”
- LinkedLinked via arxiv author · 85%Deniz Erdogmus →
“RTS Smoother-Guided Learning of Physics-Based Neural Differential Models”