AgentODE uses LLMs to discover ODE structures and infer parameter distributions from aggregate data, recovering consistent structures on benchmarks and RDEB clinical data with 231 observations from 46 patients.
arXiv preprint arXiv:2004.08424 , year=
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SINDyG extends SINDy by adding a graph-informed penalty to sparse regression, yielding more accurate and simpler models of network dynamics on Stuart-Landau oscillator networks than standard SINDy.
A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.
Numerical simulations of an Alpha Group-derived ODE system show qualitative transitions from Euclidean to non-Euclidean regimes as a rotation parameter increases from 0 to π/2.
citing papers explorer
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LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data
AgentODE uses LLMs to discover ODE structures and infer parameter distributions from aggregate data, recovering consistent structures on benchmarks and RDEB clinical data with 231 observations from 46 patients.
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SINDyG: Sparse Identification of Nonlinear Dynamical Systems from Graph-Structured Data, with Applications to Stuart-Landau Oscillator Networks
SINDyG extends SINDy by adding a graph-informed penalty to sparse regression, yielding more accurate and simpler models of network dynamics on Stuart-Landau oscillator networks than standard SINDy.
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Orthogonal Discrepancy Kernels for Learning with Partial Physics
A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.
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The Extended Alpha Group Dynamic Mapping
Numerical simulations of an Alpha Group-derived ODE system show qualitative transitions from Euclidean to non-Euclidean regimes as a rotation parameter increases from 0 to π/2.