TRACE is a graph‑network simulator that stores granular contact history on edges using attention‑based message passing and a gated recurrent unit, with an edge‑identity dictionary to preserve memory as contacts change. It predicts normal and tangential forces while enforcing Coulomb friction and internal force balance, and is trained via single‑step pretraining followed by autoregressive fine‑tuning. On 2D and 3D granular column‑collapse benchmarks, TRACE achieves significantly lower long‑rollout position and deposit errors than existing simulators, uses fewer parameters, maintains near‑zero particle interpenetration, and outperforms the material point method by 12.2× (2D) and 8.9× (3D).
By Changjian Zhou, Negin Yousefpour, Jie Qi, Junfeng Fang, Guillermo A. Narsilio, Hans Petter Jostad
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.
By Ruixin Zhou, Boliang Yu
arXiv:2109.08813v1 Announce Type: cross
Abstract: Seismic wave velocity of underground rock plays important role in detecting internal structure of the Earth. Rock physics models have long been the f...
By Weitao Sun
arXiv:2607. 14233v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE).
By Nilay Anurag, Shital Adhikari, Taniya Kapoor, Nikhil Muralidhar
arXiv:2608. 00593v1 Announce Type: cross Abstract: In stream finishing, the wear experienced by a workpiece depends strongly on its orientation within the rotating abrasive media.
By Anand Kumar, Puli Saikiran, Vineet Dawara, Koushik Viswanathan
Drift Field Net (DFN) is a deep neural network that predicts ocean surface flow fields from satellite observations, trained via a two‑stage strategy combining simulated data pretraining and Lagrangian fine‑tuning with an advection‑consistent loss. DFN improves particle trajectory forecasts, reducing mean positioning error by 20 km over a 7‑day period compared to an operational physics‑based model, and further decreasing error by 10 km when the advection loss is applied. The study demonstrates that incorporating Lagrangian constraints into deep‑learning training enhances ocean surface flow prediction accuracy.
By Th\'eo Archambault, Pierre Garcia, Mattia Romero, Anastase Charantonis, Dominique B\'er\'eziat
The paper introduces a Physics Informed Recurrent Neural Network (PIRNN) that simultaneously predicts target time series and unobservable intermediate physical variables, enhancing robustness and interpretability. It adapts to any physical model with multiple equations and variables, demonstrated on groundwater level predictions using the Gardenia model. Experiments on twelve real‑world datasets show PIRNN outperforming several neural network baselines and the Gardenia model, with an ablation study confirming the value of physical knowledge.
By Etienne Lehembre (CA, LIFO), Pascal Audigane (BRGM), Vincent Nguyen (LIFO), Christel Vrain (LIFO, CA), Thi-Bich-Hanh Dao (LIFO, CA)
arXiv:2609.39005v1 Announce Type: new
Abstract: Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During...
By Yanjie Tong, Phillip Si, Yuan Qiu, Peng Chen
arXiv:2504. 08909v2 Announce Type: replace Abstract: Digital elevation models derived from Interferometric Synthetic Aperture Radar (InSAR) data over glacial and snow-covered regions often exhibit systematic elevation errors, commonly termed "penetration bias.
By Islam Mansour, Georg Fischer, Ronny Haensch, Irena Hajnsek
arXiv:2608. 08959v1 Announce Type: new Abstract: Gravimetry images subsurface density contrasts associated with geological structures, geothermal systems, and intrusive bodies.
By Le\'on Suarez-Rodriguez, Paul Goyes-Pe\~nafiel, Javier Torres-Quintero, Henry Arguello
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
arXiv:2606. 28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensionality, memory constraints, and limited training data.
By Christian Munoz, Alexandre Tartakovsky