惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

爱范儿
爱范儿
H
Help Net Security
Jina AI
Jina AI
T
The Blog of Author Tim Ferriss
宝玉的分享
宝玉的分享
博客园 - 叶小钗
Y
Y Combinator Blog
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
WordPress大学
WordPress大学
C
Check Point Blog
Recent Announcements
Recent Announcements
IT之家
IT之家
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
美团技术团队
云风的 BLOG
云风的 BLOG
雷峰网
雷峰网
H
Hackread – Cybersecurity News, Data Breaches, AI and More
S
SegmentFault 最新的问题
MyScale Blog
MyScale Blog
Apple Machine Learning Research
Apple Machine Learning Research
Microsoft Azure Blog
Microsoft Azure Blog
V
Visual Studio Blog
B
Blog

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
Geometric Analysis of the Damped Harmonic Oscillator via ...
[Submitted on 29 May 2026] · 2026-06-01 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:The underdamped harmonic oscillator is analyzed through the complex mapping $\zeta = e^{-i\varphi}we^{-w}$ with $w = \beta t + i\Omega t$, which transforms the dynamics into a logarithmic spiral. Within this framework, the displacement extrema correspond to crossings of the imaginary axis by $\zeta(t)$, yielding the explicit times $t_n = (\theta - \varphi - \pi/2 + n\pi)/\Omega$, where $\theta = \arctan(\Omega/\beta)$. The Lambert $W$ function provides closed-form solutions $t = -\beta^{-1}W_k(-\beta A/\omega_0)$ for the times at which the spiral radius attains a given threshold $A$, covering both the rising and decaying branches. The quality factor $Q = \omega_0/(2\beta) = \tfrac{1}{2}\sec\theta$ is directly encoded in the ray angle $\theta$ of the $(u,v)$-plane. Key geometric invariants are derived: the winding number $N_\varepsilon \approx (Q/\pi)\ln(2Q/\varepsilon)$ for large $Q$, the enclosed area $A = \omega_0^2\Omega/(8\beta^3) \approx Q^3$ in the lightly damped limit, and the energy decay $E(t) = E_0 e^{-\omega_0 t/Q}$. Three methods for determining $Q$ from experimental data are compared: logarithmic decrement, ray-angle measurement, and spiral turn counting. The turn-counting method proves particularly robust for high-$Q$ systems, where successive amplitude peaks differ by tiny fractions. The framework unifies classical damped oscillations with complex analysis and special functions.

Submission history

From: Ken Roberts [view email]
[v1] Fri, 29 May 2026 15:50:13 UTC (1,340 KB)