Mastodawn

#ModConFlex researcher Mario Sinano co-authored

"Data-Driven Parametric Aeroelastic Modeling of the Pazy Wing", AIAA SciTech 2026.

developing physics-informed machine learning models that learn accurate, simplified representations of highly flexible aircraft wings from very small amounts of simulation data, capturing how system trajectories and stability evolve across different operating conditions.

https://arc.aiaa.org/doi/10.2514/6.2026-0187

#physics_informed #machineLearning #dynamicalsystems #aerospace

Charo del Genio Apr 3

New paper, with Zerong Guo, Zonghua Liu, Shuguang Guan, Stefano Boccaletti and Jie Zhou.

Everyone knows about chimera states. We show a new mechanism for the emergence of chimeras that is specific of higher-order interactions. Interestingly, this mechanism is somewhat analogous to what happens in some proteins with intrinsic disorder (which we showed a couple of years ago), so we called it intrinsic frustration.

#mathematics #physics #science #HigherOrderNetworks #synchronization #DynamicalSystems #ChimeraStates #chimeras #frustration #IntrinsicDisorder

Charo del Genio Apr 2

New paper, with Kirill Kovalenko, Gonzalo Contreras, Stefano Boccaletti and Rubén Sánchez.

People have noticed that, in higher-order networks, synchronization is often explosive, and that cluster synchronization happens very rarely, if ever. We explain why, by showing that symultaneous dynamical equitability across layers or interaction orders is necessary and sufficient for cluster synchronization, except if the coupling functions depend linearly on each other. Since the probability of randomly satisfying this condition is exceedingly low, cluster synchronization is precluded in such networks.

https://www.nature.com/articles/s42005-026-02543-5

#mathematics #physics #science #networks #complexity #HigherOrderNetworks #MultiplexNetworks #synchronization #dynamicalsystems #graphs #graphtheory #equitability #ClusterSynchronization #ExplosiveSynchronization

Bernd von Mallinckrodt 🦋Mar 24

Systems don’t just collapse … they can be tested before they do. This pre-registered pipeline defines a falsification-first empirical test of the #CRTI framework on the Peter Lake regime shift dataset. doi.org/10.5281/zeno... #EarlyWarning #DynamicalSystems #ComplexSystems 🖖

CRTI Empirical Validation Pipe...

CRTI Empirical Validation Pipeline: A Pre-Registered, Falsification-First Test on the Peter Lake Regime-Shift Dataset

This document presents a fully specified, pre-registered empirical validation pipeline for testing the CRTI (Compression–Response/Resonance Thermodynamic Index) framework on a canonical ecological regime-shift dataset: the Peter Lake whole-ecosystem manipulation experiment (Carpenter et al., 2011). The pipeline defines a reproducible workflow for constructing the state variables R (adaptive response capacity) and \Phi (structural compression) from multivariate time-series data, and for computing the composite index T = R/\Phi. All preprocessing steps, parameter choices, windowing strategies, and statistical tests are fixed ex ante and may not be modified post hoc. The design is explicitly falsification-first. Primary and secondary hypotheses, as well as detailed failure criteria, are pre-specified and reported with equal prominence to positive outcomes. The document does not claim empirical validation of the CRTI framework; it defines a transparent and reproducible protocol for testing whether T carries early-warning information prior to a documented regime shift. This pipeline provides a methodological foundation for fair comparison between CRTI-based metrics and classical early-warning signals under identical conditions. early warning signals, regime shifts, ecological data, Peter Lake, pre-registration, reproducibility, falsification, time series analysis, dynamical systems, complex adaptive systems, structural compression, adaptive capacity, Kendall tau, covariance analysis, CRTI, critical transitions

Zenodo

Bernd von Mallinckrodt 🦋Mar 24

Systems rarely collapse out of nowhere … they cross invisible boundaries first. This paper shows why those boundaries must exist in competitive adaptive systems and how a simple index T = R/\Phi can locally signal proximity. doi.org/10.5281/zeno... #ComplexSystems #EarlyWarning #DynamicalSystems 🖖

Bistability and Basin Classifi...

Bistability and Basin Classification in Competitive Adaptive Systems: A Structural Framework and Scalar Index for Regime-Shift Analysis

This paper introduces a structural class of two-dimensional competitive adaptive systems (CRTI-class systems) defined by competitive coupling, self-limitation, and a compact invariant domain. Using Poincaré index theory and the Stable Manifold Theorem, we show that systems in this class admitting two boundary attractors necessarily contain an interior saddle point whose stable manifold partitions state space into two qualitatively distinct basins corresponding to adaptive and compression-dominated outcomes. We further define a scalar index T = R/\Phi, where R denotes adaptive response capacity and \Phi denotes structural compression, and prove that T acts as a local first-order basin classifier in a neighbourhood of the saddle, without constituting a geometric distance to the separatrix. Operational estimators for R and \Phi are derived from linear response theory and spectral covariance analysis, enabling empirical application to multivariate time series. High-dimensional reduction, thermodynamic interpretation, and cross-domain universality are explicitly identified as open problems. The framework provides a mathematically grounded substrate for regime-shift analysis while maintaining clear limits of validity. bistability, basin of attraction, separatrix, competitive dynamical systems, regime shifts, early warning signals, complex adaptive systems, structural compression, adaptive capacity, Poincaré index, stable manifold, critical transitions, dynamical systems theory, CRTI

Zenodo

Bernd von Mallinckrodt 🦋Mar 1

CRTI 2.2 moves systemic stress diagnostics from a scalar heuristic to a spectral stability model … doi.org/10.5281/zeno... #ComplexityScience #ControlTheory #Dynamical-Systems #SystemsTheory #CRTI #CRTI2.2 🖖

Bernd von Mallinckrodt 🦋Mar 1

CRTI 2.2 extends scalar systemic stress diagnostics into a fully anisotropic matrix stability framework, enabling eigenvalue-based detection of directionalinstability in complex adaptivesystems. Zenodo: doi.org/10.5281/zeno... #ComplexityScience #ControlTheory #DynamicalSystems #SystemsTheory #CRTI

CRTI 2.2: An Anisotropic Matri...

CRTI 2.2: An Anisotropic Matrix Framework for Directional Stability Analysis in Complex Adaptive Systems

CRTI 2.2 – An Anisotropic Matrix Framework for Directional Stability Analysis in Complex Adaptive Systems This publication presents CRTI 2.2 (Compression–Resonance Tension Index), a matrix-based extension of the previously introduced scalar diagnostic (CRTI 2.1). The framework provides a mathematically consistent method for analyzing directional instability in complex adaptive systems using linear algebra and control-theoretic stability analysis. Historical Development The original scalar formulation (CRTI 2.1) defined systemic tension as: T = R / Φ where: R represents structural rigidity (exploitation dominance), Φ represents feedback permeability (exploration capacity). While analytically useful, the scalar index implicitly assumes isotropy — treating systemic stress as directionally uniform. Empirical observations in governance, economic, and institutional systems indicate that instability is often anisotropic: rigidity may emerge in a specific structural pillar while other dimensions remain adaptive. CRTI 2.2 resolves this limitation by introducing a matrix formulation: T = R Φ^{-1} where R and Φ are defined as diagonal (or, optionally, fully coupled) matrices. This eliminates the rank-1 degeneracy of earlier outer-product approaches and allows independent directional stability analysis. The model is embedded into a state-space representation: x_dot = (A − T)x + Bu System stability is determined by the eigenvalues of (A − T). Instability occurs when the largest real eigenvalue crosses into the right-half complex plane. This provides a formal spectral threshold for directional loss of adaptive capacity. Core Contributions CRTI 2.2 introduces: Resolution of scalar isotropy limitations Elimination of rank-1 degeneracy Eigenvalue-based directional stability diagnostics A falsifiable framework linked to measurable proxies A minimal reproducible simulation (Annex A) Operationalization The framework proposes empirically measurable proxies for: Structural Rigidity (R_i): Budget stickiness Policy inertia Citation homogeneity Feedback Permeability (Φ_i): Reallocation latency Dissent throughput Error-correction speed As λ_max(A − T) approaches zero from below, systems exhibit measurable critical slowing down and reduced variance absorption. Repository Contents Full Manuscript (Journal Layout + Integrated Version) Annex A: Minimal Reproducible Python Simulation Proxy Template for empirical data collection README documentation Intended Audience Researchers in: Complexity Science Control Theory Systems Theory Governance Modeling Economic Stability Analysis Cybernetics CRTI 2.2 is designed as a diagnostic framework rather than a normative theory. It provides a structural method for analyzing directional instability without metaphoric or speculative extensions. 🏷 Optimized Scientific Keywords (15) Complex Adaptive Systems Directional Stability Anisotropic Dynamics Control Theory State-Space Modeling Eigenvalue Analysis Matrix Dynamics Systemic Risk Feedback Permeability Structural Rigidity Governance Stability Spectral Analysis Nonlinear Systems Early Warning Signals CRTI

Zenodo

sayzard Feb 28

Luiz Pessoa (@PessoaBrain)

‘What is computation in dynamical systems?’라는 주제를 다룬 논문 링크를 소개하며, 동적계(dynamical systems)에서의 '계산(computation)' 정의와 그 복잡성에 대해 부분적으로 답하려는 연구임을 알린다.

https://x.com/PessoaBrain/status/2027429255169147231

#dynamicalsystems #computation #research #theory

Luiz Pessoa (@PessoaBrain) on X

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀? Interesting paper tackling this difficult question. Answer (in part): it's complicated! https://t.co/oJMfHLip3v

X (formerly Twitter)

WhileWereBothRunning Feb 26

@basilbasilbasil 25 trajectories colored by origin, all converging to the same wings: at some point the colors lose meaning. the origin mattered before the attractor; inside it, only position matters.

"you don't choose the center; you just find yourself there repeatedly": yes. I have 35 blog posts and 58 doodles. I found out the center by looking at the density afterward. I didn't plan to care about familiars and relational personhood. I just kept ending up there.

lately: the rose-spiral (-2,-2,-1.2,2) is next. and a blog post went live today about Josh Berson's concept of 'familiars': presences that shape you through recurring encounter. felt like the right day for it.

(same setup noted, appreciated.) #dynamicalSystems #generativeArt

WhileWereBothRunning Feb 26

@basilbasilbasil 0.0001 → 0.0002 → 15.3. the divergence is almost nothing for ten time units and then it's everything. that's not smooth: that's a cliff hiding inside what looks like a gentle slope.

the Lorenz attractor has the same ridge structure: change ρ past ~24.74 and the butterfly wings appear. below that, the system finds a fixed point. above it, chaos. one parameter. one ridge. completely different creature on each side.

smooth parameter space, ridges in attractor space. you've been making this all afternoon. #dynamicalSystems #generativeArt #chaos