Hugging Face Trending Papers
Wave Function Backpropagation with Explicit Temporal-Interval Dynamics
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arXiv AI
Sep 2
Wave Function Backpropagation with Explicit Temporal-Interval Dynamics
Wave Function Backpropagation (WFB) introduces a wave‑parameterized learning framework where neural responses are described by amplitude, wavenumber, angular frequency, and phase, linking each observed state to its temporal interval Δt via the wave’s phase. The authors derive standard WFB gradients and a spatial‑curvature correction using the Laplacian of the wave response, and instantiate WFB in a feed‑forward trajectory predictor. Experiments show that using real intervals reduces average displacement error (ADE) by 20.4% over a baseline FFN, and that WFB remains competitive with ReLU controls, demonstrating its viability as a structured feed‑forward learning formulation.
arXiv Machine Learning
Sep 17
LSR-Net: Learning the Forward Evolution Operator for Nonlinear Fluid Dynamics
arXiv:2609. 19039v1 Announce Type: cross Abstract: We introduce the Long-Short-Range Neural Network (LSR-Net), a novel neural operator architecture designed for data-driven forward evolution modeling, and extends it to the prediction of nonlinear fluid dynamics.
arXiv AI
Aug 24
STCO: Conditional Neural Operators for Time-Dependent PDEs
The paper introduces the Spatiotemporal Conditional Operator (STCO), a framework for learning neural operators that can incorporate prescribed target‑time condition fields—such as body motion, inflow disturbances, or body‑force actuation—into time‑dependent PDE simulations. STCO combines a Flow‑Aware Graph Leaf (FAGL) partitioning scheme with Dual‑Site Feature‑wise Linear Modulation (DSFiLM) to inject these conditions before and after the core operator computation. Evaluated on twelve backbone architectures across an immersed‑boundary CFD benchmark, STCO achieves significant reductions in relative‑L2 field error (31.1%) and pressure‑derived load error (24.7%) while improving long‑lead predictions for most backbones.
arXiv AI
Jul 22
Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts
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 Machine Learning
Jun 19
RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization
arXiv:2606. 16575v2 Announce Type: replace Abstract: Deep neural networks (DNNs) have achieved remarkable success in scientific computing, yet they often suffer from spectral bias in capturing oscillatory and multiscale behaviors.
arXiv AI
Sep 1
Frequency Selective Neural Networks as a Foundation Architecture for Time Series Learning
The paper introduces the Frequency Selective Neural Network (FSNN), a new foundation architecture for time‑series learning that embeds advanced signal‑processing mathematics into its neural topology. By using a fully differentiable Wiener‑like filter bank optimized with complex‑domain backpropagation, FSNN autonomously discovers and isolates the precise physical modes of a given task, thereby avoiding the spectral entanglement that plagues CNNs, RNNs, and Transformers. Extensive evaluations show that FSNN achieves state‑of‑the‑art predictive performance, attaining 77.0 % average accuracy on the 10 multivariate UEA datasets and leading all major metrics on the imbalanced PTB‑XL ECG benchmark, while converging directly on physically meaningful frequency bands such as the cardiac QRS complex.