FLUID is a continuous-time transformer using Liquid Attention Networks to model attention as stable ODE solutions that interpolate between discrete SDPA and CT-RNNs, with an explicit sink gate and liquid hyper-connections for better information flow.
Odeformer: Symbolic regression of dynamical systems with transformers
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DYSCO jointly recovers latent trajectories and governing equations from noisy observations via multi-view contrastive learning, with theoretical guarantees up to affine indeterminacy.
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.
Latent Grammar Flow embeds grammar-based ODE representations into a discrete latent space with a behavioural loss and samples candidate equations via discrete flow to fit observed data.
S-MNN reformulates Mechanistic Neural Networks to achieve linear computational complexity for long sequences while preserving accuracy and interpretability.
citing papers explorer
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FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning
FLUID is a continuous-time transformer using Liquid Attention Networks to model attention as stable ODE solutions that interpolate between discrete SDPA and CT-RNNs, with an explicit sink gate and liquid hyper-connections for better information flow.
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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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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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Neuro-Symbolic ODE Discovery with Latent Grammar Flow
Latent Grammar Flow embeds grammar-based ODE representations into a discrete latent space with a behavioural loss and samples candidate equations via discrete flow to fit observed data.
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Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
S-MNN reformulates Mechanistic Neural Networks to achieve linear computational complexity for long sequences while preserving accuracy and interpretability.