Paper traces Granger tests toward causal structure in dynamical models
The review says Granger tests detect predictive links among signals, not causal links among generating mechanisms.
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18 stories, newest first · one card per story
Stories tagged #dynamical-systems
Staged closure learning offers efficiency gains
Staged approach matches embedded accuracy with fewer evaluations in closure modeling.
Velocity Matching Objective Improves Dynamical System Learning
Simulation-free velocity matching approximates continuous-time evolution without rollout simulation or Jacobian constraints.
FIDE ranks experimental designs by reference-relative law revision
It completes aggregate constraints against a frozen baseline and prefers the design needing the least dynamical correction.
Random features approximate Koopman generators of continuous-time systems
A data-informed dictionary for gEDMD is reported faster than iterative optimization, with convergence proofs and a boundedness counterexample.
LEAP learns actions from trajectory observations
LEAP anchors unknown actions to reference processes for reusable objectives in scientific simulations.
Neural method tracks how local conservation laws break down globally
Expanding-domain neural deflation recovers the expected local invariants, then their successive breakdown.
Identifiability of a dissipative knowledge-dynamics model: exact recovery under designed excitation, degeneration on observational data
Researchers prove a dissipative learning model is identifiable only under designed excitation, not observational data.
From Redundancy to Minimality: Fixed-Point-Guided Hierarchical Reduction of Learned Piecewise-Linear Dynamics
Fixed-point-guided reduction turns redundant AL-RNNs into minimal dynamics, lifting Chua success from 20% to 71%.
Port-Hamiltonian Neural Networks for Systems with Multiple Asymptotically Stable Equilibria
Port-Hamiltonian networks are extended to multiple stable equilibria using Bregman divergences.
Disentangling Computation in Multi-Task Neural Networks with the Green's Operator
Researchers use the Green's operator to map how multitask recurrent networks reuse computation across tasks and time.
Dynamics to decision: A mathematical theory of Lyapunov spectra and decision boundaries in deep classifiers
Paper ties finite-time Lyapunov exponents of deep classifiers to decision-boundary geometry.
Learning Chaos Without Seeing Chaos: Extrapolation of Global Dynamics in Autoregressive Transformers
Small autoregressive transformers recover unseen chaotic dynamics, including a near-Feigenbaum logistic-map scaling ratio.
Statistical Learning of Contractive Dynamical Representations for Composite Adaptive Control
A hard-EM method learns contractive disturbance models for predictive composite adaptive tracking control.
Nonequilibrium Phases of Repulsive Self-Attention: Chaos, Attention Condensation, and Emergent Locality
Researchers show repulsive self-attention in a minimal recurrent transformer produces chaos, condensation, and emergent locality.
LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials
Researchers propose LEAP certificates as robust control-barrier safety filters for uncertain robotic systems.
Local gradient neural operator
Researchers propose LGNO, a lightweight interpretable neural operator using learnable local stencils, matching global-operator accuracy on PDE benchmarks with fewer parameters.
Variational Continuation for Double Pendulum Periodic Orbits
Researchers introduce a Hessian-based, integrator-free method using automatic differentiation to continue periodic orbits in the double pendulum, uncovering previously unreported orbit families.