PSTNet: Physically-Structured Turbulence Network

• URL: http://arxiv.org/abs/2603.07957v1
• Date: Mon, 09 Mar 2026 04:46:46 GMT
• Title: PSTNet: Physically-Structured Turbulence Network
• Abstract summary: Physically-Structured Turbulence Network (PSTNet) is a lightweight architecture that embeds physics directly into its structure.<n>PSTNet achieves a mean miss-distance improvement of +2.8% with a 78% win rate and a statistically significant effect size.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable real-time estimation of atmospheric turbulence intensity remains an open challenge for aircraft operating across diverse altitude bands, particularly over oceanic, polar, and data-sparse regions that lack operational nowcasting infrastructure. Classical spectral models encode climatological averages rather than the instantaneous atmospheric state, and generic ML regressors offer adaptivity but provide no guarantee that predictions respect fundamental scaling laws. This paper introduces the Physically-Structured Turbulence Network (PSTNet), a lightweight architecture that embeds physics directly into its structure. PSTNet couples four components: (i) a zero-parameter backbone derived from Monin-Obukhov theory, (ii) a regime-gated mixture of specialist sub-networks supervised by Richardson-number-derived soft targets, (iii) Feature-wise Linear Modulation layers conditioning hidden representations on local air-density ratio, and (iv) a Kolmogorov output layer enforcing inertial-subrange scaling as an architectural constraint. The entire model contains only 552 learnable parameters, requiring fewer than 2.5 kB of storage and executing in under 12s on a Cortex-M7 microcontroller. We validate PSTNet on 340 paired six-degree-of-freedom guidance simulations spanning three vehicle classes (Mach 2.8, 4.5, and 8.0) and six operational categories with real-time satellite weather ingestion. PSTNet achieves a mean miss-distance improvement of +2.8% with a 78% win rate and a statistically significant effect size. Our results demonstrate that encoding domain physics as architectural priors yields a more efficient and interpretable path to turbulence estimation accuracy than scaling model capacity, establishing PSTNet as a viable drop-in replacement for legacy look-up tables in resource-constrained, safety-critical on-board guidance systems.

Related papers

• Real-Time Semantic Segmentation with Optimized RetinaNet Architectures for Embedded Automotive Systems [0.0]
  This paper presents an optimized semantic segmentation architecture derived from the RetinaNet detection framework.<n> Opt-RetinaSeg replaces the standard ResNet-50 backbone with a hybrid lightweight feature extractor.<n>The proposed model achieves 73.9% mIoU at 70.4 FPS, representing a 7.4x inference speedup and a 4x reduction in model size relative to the ResNet-50 baseline.
  arXiv  Detail & Related papers  (2026-07-21T08:30:00Z)
• LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation [53.88286427894783]
  Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context.<n>Yet, their computational footprint typically confines them to cloud execution, hindering low-latency inference with local deployment on resource-constrained robots.<n>We present a distillation strategy that transfers complex spatial-semantic reasoning from large frontier models into a lightweight, 4B- parameter local VLM for edge execution on embedded GPU devices.
  arXiv  Detail & Related papers  (2026-06-26T09:11:59Z)
• Position: Weight Space Should Be a First-Class Generative AI Modality [67.10702320971893]
  This paper argues that model checkpoints should be treated as a first-class data modality.<n>It argues that generative modeling in weight space should be standardized as a core machine learning primitive.<n>Our goal is to shift the community's default mindset from optimizing models per task to sampling models from learned weight distributions.
  arXiv  Detail & Related papers  (2026-05-18T16:38:26Z)
• VeloxNet: Efficient Spatial Gating for Lightweight Embedded Image Classification [1.9903316442426757]
  This paper introduces VeloxNet, a lightweight CNN architecture that replaces SqueezeNet's fire modules with gated multi-layer perceptron blocks for embedded image classification.<n>We evaluate VeloxNet on three aerial image datasets.
  arXiv  Detail & Related papers  (2026-03-19T21:46:20Z)
• KAN-FIF: Spline-Parameterized Lightweight Physics-based Tropical Cyclone Estimation on Meteorological Satellite [22.99496587694237]
  Timely monitoring of tropical cyclones is crucial for reducing loss of life and property.<n>Current physics-guided models suffer from linear feature interactions that fail to capture high-order relationships between TC attributes.<n>This study introduces the Kolmogorov-Arnold Network-based Network-based Interaction Feature Framework (KAN-FIF)
  arXiv  Detail & Related papers  (2026-02-12T16:07:39Z)
• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)
• Adaptive Fusion Graph Network for 3D Strain Field Prediction in Solid Rocket Motor Grains [4.379802056393599]
  Local high strain in solid rocket motor grains is a primary cause of structural failure.<n>This paper proposes an adaptive graph network, GrainGNet, to preserve the key mechanical features of structurally critical regions.<n>GrainGNet reduces the mean squared error by 62.8% compared to the baseline graph U-Net model.
  arXiv  Detail & Related papers  (2025-12-29T13:02:04Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale [91.84761739154366]
  FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting.<n>FourCastNet 3 delivers forecasting accuracy that surpasses leading conventional ensemble models and rivals the best diffusion-based methods.<n>Its computational efficiency, medium-range probabilistic skill, spectral fidelity, and rollout stability at subseasonal timescales make it a strong candidate for improving meteorological forecasting and early warning systems through large ensemble predictions.
  arXiv  Detail & Related papers  (2025-07-16T11:22:18Z)
• A Dynamic Fuzzy Rule and Attribute Management Framework for Fuzzy Inference Systems in High-Dimensional Data [7.960582417281041]
  This paper presents an Adaptive Dynamic Attribute and Rule framework designed to address the challenges posed by high-dimensional data in neuro-fuzzy inference systems.<n>By integrating dual weighting mechanisms, ADAR adaptively streamlines complex fuzzy models without sacrificing performance or interpretability.<n> Experimental evaluations on four diverse datasets show that ADAR-based models achieve consistently lower Root Mean Square Error (RMSE) compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-04-27T08:02:10Z)
• DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers [105.74546828182834]
  We show a hardware-efficient dynamic inference regime, named dynamic weight slicing, which adaptively slice a part of network parameters for inputs with diverse difficulty levels. We present dynamic slimmable network (DS-Net) and dynamic slice-able network (DS-Net++) by input-dependently adjusting filter numbers of CNNs and multiple dimensions in both CNNs and transformers.
  arXiv  Detail & Related papers  (2021-09-21T09:57:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.