The study investigates granular slip forecasting by separating material state, loading progress, and window geometry effects using a compact neural score based on stress, pressure, coordination, non-affine motion, and force-network observables. Trained on 36 shear trajectories and tested on 18 new ones, the model ranked near‑slip windows better than prevalence or phase controls, yet loading‑history coordinates (e.g., causal elapsed strain) outperformed the compact score. The findings show that while compact observables contain temporally aligned slip information, stronger loading‑history baselines and geometry sensitivity limit the identification of a state‑specific short‑horizon precursor.
arXiv:2609.05542v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche d...
arXiv:2608. 15483v1 Announce Type: new Abstract: Modern deep networks are trained through long update trajectories, yet their temporal organization remains less systematically characterized than architectures, losses, or optimizers.
Modern deep networks are trained through long update trajectories, yet their temporal organization remains less systematically characterized than architectures, losses, or optimizers. We study short-h...
arXiv:2607. 19060v1 Announce Type: cross Abstract: Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and manipulation tasks.
arXiv:2607. 23667v1 Announce Type: cross Abstract: A flow surrogate validated on a simple regime is often taken as evidence that the approach will carry to a richer one.
arXiv:2606. 17572v1 Announce Type: new Abstract: Learned dynamics models often answer global physical questions, such as fault severity or impact stiffness, by pooling a per-step feature sequence into one readout vector.
The paper introduces a diagnostic protocol to examine how passive object-state world models encode event-conditioned latent physical structure. It evaluates GRU, Transformer-lite, and RSSM-lite models on a dataset featuring free motion, collision, and occlusion events, finding that each architecture learns predictive dynamics and that event context shifts the emphasis among kinematic, contact, and object-permanence readouts. Functional sensitivity tests reveal contact-related structure during collisions and object-permanence structure during occlusions, supporting the presence of event-conditioned latent structure without explicit physical modules.
arXiv:2608. 20009v1 Announce Type: new Abstract: Understanding object dynamics requires not only predicting future trajectories but also examining whether a model captures the physical properties that govern motion.
The paper introduces a mechanism‑aware conditioning framework that uses a nudged coarse ensemble to capture local instability geometry in chaotic systems. By injecting ensemble covariance statistics via a small FiLM module, the authors enhance rare‑event emulation in both a low‑dimensional chaotic benchmark and a quasi‑geostrophic flow model, achieving significant improvements in exceedance‑frequency and tail‑density errors with limited data. The approach demonstrates that local instability information can be leveraged as a practical conditioning signal for data‑efficient emulation of extreme events.
Generative AI emulators are increasingly used in scientific domains where we already have strong theory, benchmarks, and physical intuition. This raises a central evaluation and interpretability question: when a foundation-style model can reproduce known continuum dynamics, what internal mechanism supports that behavior, is the internal behaviour consistent with known physics, and how does it relate to where the emulator succeeds or fails?
arXiv:2606. 11657v1 Announce Type: cross Abstract: Generative AI emulators are increasingly used in scientific domains where we already have strong theory, benchmarks, and physical intuition.
arXiv:2607. 16821v1 Announce Type: cross Abstract: Task arithmetic, sequential fine-tuning, activation steering, and first-order random search all operate through relatively small perturbations around an already trained checkpoint, and they rely on different local approximations: individual perturbations should be first-order predictable, task updates should compose with controlled interference, useful tangent structure should be stable and possible to estimate, and weight edits should have counterparts in representation space.