Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN

• URL: http://arxiv.org/abs/2607.12409v1
• Date: Tue, 14 Jul 2026 06:35:50 GMT
• Title: Mechanical Analysis of Parachute Suspension Line Deployment with Binding Tapes Using PINN
• Abstract summary: This paper develops a physics-informed neural network (PINN) algorithm for tension prediction during line extraction and straightening.<n>It outperforms traditional integration methods in both computational efficiency and numerical accuracy.
• Score: 8.412266694861163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parachutes are widely utilized in aviation, aerospace and lifesaving missions. As the initial stage of parachute deployment, suspension line extraction and straightening directly determines the smooth implementation of subsequent inflation procedures. This ultra-short process involves intricate dynamic load variations. Most existing studies adopt numerical integration of ordinary differential equations to calculate line tension, yet this method fails to rapidly acquire tension values at arbitrary positions along suspension lines. This paper develops a physics-informed neural network (PINN) algorithm for tension prediction during line extraction and straightening, which outperforms traditional integration methods in both computational efficiency and numerical accuracy. Furthermore, the regulatory law of binding tape parameters on line dynamic tension is investigated. Comparative validations against flight test data and conventional numerical results verify the reliability and effectiveness of the proposed PINN framework.

Related papers

• X-ACTA: eXtended Analytic Center Tension distribution Algorithm for fixed and mobile cable-driven-parallel-robot [0.0]
  This paper introduces an extended version of the Analytic Center method as a criterion for selecting cable tensions outside the Wrench-Feasible Workspace (WFW)<n> Notably, the proposed method maintains continuous and differentiable tension profiles, ensures fast real-time convergence to a unique solution, and relegates wrench errors to a negligible area of the WFW.
  arXiv  Detail & Related papers  (2026-07-09T09:07:56Z)
• Taming Curvature: Architecture Warm-Up for Stable Transformer Training [52.6838951211596]
  We introduce a fast online estimator of the largest (preconditioned) Hessian eigenvalue (i.e., curvature) based on a warm-started variant for power with Hessian-vector products.<n>We find that training instabilities coincide with surges in preconditioned curvature and that curvature grows with depth.<n> Experiments on large Transformers validate that our approach enables efficient curvature tracking and reduces instabilities.
  arXiv  Detail & Related papers  (2026-06-15T14:16:56Z)
• An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts [2.2940141855172036]
  nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind linear control allocation techniques.<n>This paper addresses these limitations by learning an explicit, constrained analytical model of the control effectiveness mapping from representative flight data.<n>The resulting mapping is compact, interpretable, and admits analytical derivatives, enabling efficient computation within solvers.
  arXiv  Detail & Related papers  (2026-06-11T18:04:43Z)
• PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective [52.693471818837395]
  We argue that PEFT should be assessed through the stability-plasticity dilemma.<n>We introduce PEFT-Arena, a benchmark that measures downstream performance and general capability retention.<n>Inspired by this, we present case studies of a post-hoc improvement with path-wise rewinding.
  arXiv  Detail & Related papers  (2026-05-27T17:59:51Z)
• Non-Parametric Rehearsal Learning via Conditional Mean Embeddings [88.89267783967263]
  We propose a non-parametric rehearsal learning approach for textitavoiding undesired future (AUF) problems.<n>Specifically, we use kernel machinery to reformulate the AUF objective into a unified representation that disentangles desirability modeling from action-induced distributional changes.
  arXiv  Detail & Related papers  (2026-05-09T15:30:52Z)
• Curvature-Guided LoRA: Steering in the pretrained NTK subspace [60.35296431630704]
  We introduce the prediction alignment problem, aiming to match the predictor obtained via PEFT to that of full fine-tuning at the level of outputs.<n>We show that this objective naturally leads to a curvature-aware, second-order formulation, where optimal low-rank updates correspond to a Newton-like, curvature-whitened gradient.<n>Based on this insight, we propose Curvature-Guided LoRA (CG-LoRA), which selects and scales adaptation directions using local curvature information.
  arXiv  Detail & Related papers  (2026-03-31T14:46:39Z)
• Accelerated Spline-Based Time-Optimal Motion Planning with Continuous Safety Guarantees for Non-Differentially Flat Systems [6.862015448582809]
  State-of-the-art approaches formulate spline-based motion planning as a single Control Problem (OCP)<n>By treating separation as an independent classification problem solvable via a linear system or quadratic program, the proposed method eliminates hyperplane parameters from the OCP variables.<n> Experimental validation demonstrates that this decoupled approach reduces trajectory times up to almost 60% compared to fully-rich environments.
  arXiv  Detail & Related papers  (2026-03-25T09:53:04Z)
• PRISM: Parallel Residual Iterative Sequence Model [52.26239951489612]
  We propose PRISM (Parallel Residual Iterative Sequence Model) to resolve this tension.<n>PRISM introduces a solver-inspired inductive bias that captures key structural properties of multi-step refinement in a parallelizable form.<n>We prove that this formulation achieves Rank-$L$ accumulation, structurally expanding the update manifold beyond the single-step Rank-$1$ bottleneck.
  arXiv  Detail & Related papers  (2026-02-11T12:39:41Z)
• Physics-informed Gaussian Processes for Safe Envelope Expansion [35.86317155999886]
  We propose a novel approach to flight test analysis using Gaussian processes (GPs) with physics-informed mean functions.<n>We demonstrate our method by estimating the pitching moment coefficient without requiring predefined or repeated flight test points.
  arXiv  Detail & Related papers  (2025-01-02T01:37:15Z)
• Learning Controlled Stochastic Differential Equations [61.82896036131116]
  This work proposes a novel method for estimating both drift and diffusion coefficients of continuous, multidimensional, nonlinear controlled differential equations with non-uniform diffusion. We provide strong theoretical guarantees, including finite-sample bounds for (L2), (Linfty), and risk metrics, with learning rates adaptive to coefficients' regularity. Our method is available as an open-source Python library.
  arXiv  Detail & Related papers  (2024-11-04T11:09:58Z)
• Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators [9.336308366735656]
  We propose a new data-driven methodology for predicting intervals of post-fault voltage trajectories in power systems. The proposed operator regression model maps the observed portion of the voltage trajectory to its unobserved post-fault trajectory. We evaluate the performance of the proposed methodology using the New England 39-bus test system.
  arXiv  Detail & Related papers  (2024-10-31T17:20:13Z)

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.