arXiv AI
Jul 7
When is a System Discoverable from Data? Discovery Requires Chaos
arXiv:2511. 08860v2 Announce Type: replace-cross Abstract: The deep learning revolution has spurred a rise in advances of using AI in sciences.
Attractor Geometry Determines the Identifiability Limits of System Discovery
arXiv:2607. 18490v1 Announce Type: new Abstract: Symbolic discovery of governing equations from data is limited not only by algorithm design and data volume, but by the geometry of the attractor: what the long-run dynamics allow to be recovered.
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arXiv Machine Learning
Jun 2
Learning Chaotic Dynamics through Second-Order Geometric Supervision
arXiv:2606. 01596v1 Announce Type: cross Abstract: Learning chaotic dynamical systems from data requires more than short-term predictive accuracy: the learned model must preserve the attractor geometry and its invariant statistics.
arXiv Machine Learning
Jul 31
On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems
arXiv:2607. 28080v1 Announce Type: cross Abstract: We extend a recently introduced Entropy-Optimal Manifold Clustering (EOMC) to allow for a joint simultaneous identification of subsets and subspaces of relevant features in nonstationary and nonlinear regression problems.
arXiv Machine Learning
Jul 17
An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
arXiv:2607. 15077v1 Announce Type: new Abstract: Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known.
arXiv Machine Learning
Jun 5
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
arXiv:2606. 05191v1 Announce Type: new Abstract: Data-driven equation discovery is fundamentally an inverse problem that seeks to infer the governing differential equations of a system directly from time-series measurements.
arXiv AI
Jun 16
Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
arXiv:2605. 09169v2 Announce Type: replace-cross Abstract: A Mamba state-space model trained only for next-step prediction appears to recover Granger-causal structure through a simple readout $S = |W_{out} W_{in}|$, with early experiments suggesting the phenomenon generalized across architectures and benefited from interventional data at $p < 10^{-5}$.
arXiv AI
Jul 31
Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression
arXiv:2607. 26528v1 Announce Type: cross Abstract: Symbolic regression provides analytical expressions, but it is usually applied one output at a time.
arXiv Machine Learning
Aug 11
Support Selection Beyond Smooth DAG Exactness: Completion Geometry,Score Margins, and Selective Certificates
arXiv:2608. 08103v1 Announce Type: new Abstract: Smooth acyclicity constraints answer whether a weighted support is a DAG, whereas structure learning asks which support change should be made.
arXiv AI
Jun 10
Between Amnesia and Chaos: A Memory Stability Expressivity Trilemma for Trainable Dissipative Oscillator Networks
arXiv:2606. 09929v1 Announce Type: cross Abstract: Physical reservoir computing harnesses nonlinear mechanical dynamics but, by convention, freezes the substrate and trains only a linear readout, presuming the substrate is not usefully trainable.
arXiv AI
Jul 16
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
arXiv:2607. 13608v1 Announce Type: new Abstract: Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe.
arXiv Machine Learning
Aug 12
Intrinsic Structure: Spectral Identifiability for Mechanistic Interpretability
arXiv:2608. 10172v1 Announce Type: new Abstract: Mechanistic interpretability explains models by identifying circuits inside them, but has no way to tell whether a circuit is a property of the model or an artifact of the method that found it.
arXiv AI
Jul 21
The Curvature Shadow: An Apparent Failure of Maximum-Entropy Equilibrium Selection is a Removable Artifact
arXiv:2607. 17543v1 Announce Type: new Abstract: In two-player zero-sum games whose Nash equilibria form a convex set, regularized solvers such as Regularized Nash Dynamics (R-NaD) empirically select the maximum-entropy member: the information projection (I-projection) of a uniform reference onto the Nash set.