← Back to all news
arXiv Machine Learning October 23, 2026 By Tobias G\"obel, Miranda C. N. Cheng

Bifurcations from Weight Space: JEPAWG on Parametric Dynamical Systems

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

  • rag
  • fine-tuning

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv Machine Learning
Jul 9

Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories

arXiv:2607. 07127v1 Announce Type: cross Abstract: Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in materials.

By Tobias G\"obel, Julian R. Ebelt, Zier Mensch, Mathis Gerdes, Miranda C. N. Cheng
ragdiffusionsafety
More like this →
arXiv AI
Oct 5

Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability

arXiv:2610.03000v1 Announce Type: cross Abstract: Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons (MLPs) require dynamic re-training with...

By Ambarish Moharil
rag
More like this →
arXiv Machine Learning
Jun 11

Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems

arXiv:2606. 11251v1 Announce Type: new Abstract: Many multivariate dynamical systems are observed only through trajectories, leaving the mechanisms governing their joint dynamics hidden.

By Xingji Cui
benchmarks
More like this →
arXiv Machine Learning
Sep 30

Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems

arXiv:2608. 22277v3 Announce Type: replace Abstract: Deep learning surrogates have become powerful tools for simulating and forecasting complex dynamical systems, yet their utility remains limited by catastrophic error accumulation during long-term autoregressive rollouts.

By Zhou Fang, Gianmarco Mengaldo
benchmarks
More like this →
arXiv Machine Learning
Jun 30

Scalar Representations of Neural Network Training Dynamics

arXiv:2606. 30384v1 Announce Type: new Abstract: Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape.

By Pedro Jim\'enez-Gonz\'alez, Miguel C. Soriano, Lucas Lacasa
rag
More like this →
arXiv Machine Learning
Aug 4

HyperODE: Zero-Shot Surrogate for Simulation and Inference of Dynamical Systems

arXiv:2608. 00852v1 Announce Type: new Abstract: Understanding and controlling complex dynamical systems often requires executing thousands of numerical simulations across vast parametric landscapes, which is time-consuming.

By Ajitesh Srivastava
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea