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.
By Byunggu Yu, Justin Kim
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.
By Qian Hou, Sutrisno, Yuqing Li, Zecheng Gan
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.
By Xingxin Yang, Zhan Zhang, Juan Li
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.
By Ali Maghami, Merten Stender, Michele Ciavarella, Antonio Papangelo
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.
By Yong Wang, Tao Zhou, Xuhui Meng
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.
By Hui Huang, Ye Sun, Shiyan Hu
arXiv:2606. 16575v1 Announce Type: new 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.
By Yong Wang, Tao Zhou, Xuhui Meng
arXiv:2602. 18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for online local learning on neuromorphic and edge hardware.
By Tom Potter, Oliver Rhodes
arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.
By Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi
ROMNet is a hybrid reduced‑order modeling and machine‑learning framework designed to improve waveform inversion for acoustic waves. It replaces the costly nonlinear mapping from a reduced‑order model (ROM) matrix to wave speed with a neural network that outputs a simpler ROM matrix, thereby reducing computational effort. The method is validated on two training datasets—random Gaussian‑based media and the GeoFWI benchmark—and compared against direct ROM inversion and two deep‑learning FWI approaches, Fourier‑DeepONet and InversionNet.
By Liliana Borcea, Alexander Mamonov, Kui Ren, Haizhao Yang, Chugang Yi
arXiv:2606. 09756v1 Announce Type: new Abstract: Responses to perturbations are key to understanding physical systems.
By Kyungeun Kim, Amanuel Anteneh, Israel Klich, Olivier Pfister, J. M. Schwarz
arXiv:2607. 18309v1 Announce Type: new Abstract: Unsteady aerodynamic phenomena, such as gusts, turbulence, and fluid-structure interactions affect an aircraft during flight.
By Henrik Lange, Reik Thormann, Philipp Bekemeyer