Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
A prediction market design that uses online learning to adaptively mix liquidity regimes from cost functions, achieving switching-regret bounds against the best hindsight sequence.
An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
A framework uses stance detection, linear dimensionality reduction, and neural potential landscapes to recover a 3D stance space explaining 45% variance and to visualize large-scale shifts across platforms and years.
LLMs disperse meaning-preserving prompts internally instead of clustering them, which produces an excessively high upper bound on output log-probability differences via Taylor expansion and Cauchy-Schwarz.
citing papers explorer
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Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
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Adaptive Liquidity in Prediction Markets via Online Learning
A prediction market design that uses online learning to adaptively mix liquidity regimes from cost functions, achieving switching-regret bounds against the best hindsight sequence.
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Adaptive Kernel Ridge Regression with Linear Structure: Sharp Oracle Inequalities and Minimax Optimality
An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
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Mapping the Winds of Stance Dynamics using Potential Landscape Models
A framework uses stance detection, linear dimensionality reduction, and neural potential landscapes to recover a 3D stance space explaining 45% variance and to visualize large-scale shifts across platforms and years.
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Understanding the Prompt Sensitivity
LLMs disperse meaning-preserving prompts internally instead of clustering them, which produces an excessively high upper bound on output log-probability differences via Taylor expansion and Cauchy-Schwarz.