A unified online algorithm predicts any LDS with Õ(k) parameters (k = instability complexity), matching lower bound, and beats equal-budget baselines on high-d systems.
Nathan and Brunton, Steven L
11 Pith papers cite this work, alongside 37 external citations. Polarity classification is still indexing.
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2026 11representative citing papers
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
Entropic Autoencoders mitigate posterior collapse by implicitly defining priors via entropy in a free-energy-minimizing encoder ensemble, yielding multimodal latent distributions that preserve data structure on reaction-diffusion, MNIST, and CelebA.
Deep-Koopman-KANDy recovers symbolic Koopman dictionaries post-training by replacing the encoder and decoder with KANs and applying a level-set construction with chain-rule gradients, achieving high recall on Lorenz and expected behavior on other maps.
Unsupervised symmetry discovery via shallow group-convolutional networks recovers latent domains from linear measurements of random fields by learning symmetry actions under stationarity and locality constraints.
DYSCO jointly recovers latent trajectories and governing equations from noisy observations via multi-view contrastive learning, with theoretical guarantees up to affine indeterminacy.
A WLaSDI-based framework creates noise-robust latent surrogates for PDE-constrained optimization, deriving direct and adjoint gradients to achieve up to five orders of magnitude speedup on radiative transfer, Vlasov-Poisson, and Burgers benchmarks.
AutoSINDy automatically builds a tailored basis library from PySR symbolic regression and applies SINDy to recover ground-truth nonlinear dynamics with 92.8% success under noise.
AC-SINDy replaces explicit feature libraries in SINDy with arithmetic-circuit compositions and adds latent-state inference with multi-step supervision to recover governing equations more scalably on noisy nonlinear systems.
Bayesian-ARGOS is a hybrid frequentist-Bayesian method that discovers equations from limited noisy observations more efficiently than SINDy or bootstrap-ARGOS while adding uncertainty quantification.
A case study develops a sparse dictionary learning approach to model pediatric asthma exacerbations from multiple risk factors and reports consensus on relative risks across statistical and machine learning models.
citing papers explorer
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A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems
A unified online algorithm predicts any LDS with Õ(k) parameters (k = instability complexity), matching lower bound, and beats equal-budget baselines on high-d systems.
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How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
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Entropic Auto-Encoding via Implicit Free-Energy Minimization
Entropic Autoencoders mitigate posterior collapse by implicitly defining priors via entropy in a free-energy-minimizing encoder ensemble, yielding multimodal latent distributions that preserve data structure on reaction-diffusion, MNIST, and CelebA.
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Deep-Koopman-KANDy: Dictionary Discovery for Deep-Koopman Operators with Kolmogorov-Arnold Networks for Dynamics
Deep-Koopman-KANDy recovers symbolic Koopman dictionaries post-training by replacing the encoder and decoder with KANs and applying a level-set construction with chain-rule gradients, achieving high recall on Lorenz and expected behavior on other maps.
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Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery
Unsupervised symmetry discovery via shallow group-convolutional networks recovers latent domains from linear measurements of random fields by learning symmetry actions under stationarity and locality constraints.
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Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning
DYSCO jointly recovers latent trajectories and governing equations from noisy observations via multi-view contrastive learning, with theoretical guarantees up to affine indeterminacy.
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Time-Dependent PDE-Constrained Optimization via Weak-Form Latent Dynamics
A WLaSDI-based framework creates noise-robust latent surrogates for PDE-constrained optimization, deriving direct and adjoint gradients to achieve up to five orders of magnitude speedup on radiative transfer, Vlasov-Poisson, and Burgers benchmarks.
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Discovery of Nonlinear Dynamics with Automated Basis Function Generation
AutoSINDy automatically builds a tailored basis library from PySR symbolic regression and applies SINDy to recover ground-truth nonlinear dynamics with 92.8% success under noise.
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AC-SINDy: Compositional Sparse Identification of Nonlinear Dynamics
AC-SINDy replaces explicit feature libraries in SINDy with arithmetic-circuit compositions and adds latent-state inference with multi-step supervision to recover governing equations more scalably on noisy nonlinear systems.
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Fast and principled equation discovery from chaos to climate
Bayesian-ARGOS is a hybrid frequentist-Bayesian method that discovers equations from limited noisy observations more efficiently than SINDy or bootstrap-ARGOS while adding uncertainty quantification.
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Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
A case study develops a sparse dictionary learning approach to model pediatric asthma exacerbations from multiple risk factors and reports consensus on relative risks across statistical and machine learning models.