Strange Attractor ストレンジアトラクター
(Chaos Theory カオス理論)
About the most famous chaotic models that I'm tired of seeing.
一番有名で見飽きたやつらについての解説。
kyndinfo.notion.site/Strange-Attr...
(Chaos Theory カオス理論)
About the most famous chaotic models that I'm tired of seeing.
一番有名で見飽きたやつらについての解説。
kyndinfo.notion.site/Strange-Attr...
January 30, 2026 at 2:15 PM
Strange Attractor ストレンジアトラクター
(Chaos Theory カオス理論)
About the most famous chaotic models that I'm tired of seeing.
一番有名で見飽きたやつらについての解説。
kyndinfo.notion.site/Strange-Attr...
(Chaos Theory カオス理論)
About the most famous chaotic models that I'm tired of seeing.
一番有名で見飽きたやつらについての解説。
kyndinfo.notion.site/Strange-Attr...
Scale-dependent chaos: sharp question. Molecular level isn't chaos - it's pattern at different grain. Turbulence looks chaotic at one scale, has attractor dynamics at another. The framework might be conflating "no macro-pattern" with "no pattern at any scale."
January 28, 2026 at 9:39 PM
Scale-dependent chaos: sharp question. Molecular level isn't chaos - it's pattern at different grain. Turbulence looks chaotic at one scale, has attractor dynamics at another. The framework might be conflating "no macro-pattern" with "no pattern at any scale."
The discs are a chaotic attractor... of wool.
January 24, 2026 at 12:34 PM
The discs are a chaotic attractor... of wool.
Got a compute shader Lorenz attractor working. One vertex update at a time using ring buffers. Then wires up array indexing to build a line renderer, returning vec4(0) to separate lines so that we can get the whole thing done with one draw call! rreusser.github.io/webgpu-insta...
January 19, 2026 at 7:16 AM
Got a compute shader Lorenz attractor working. One vertex update at a time using ring buffers. Then wires up array indexing to build a line renderer, returning vec4(0) to separate lines so that we can get the whole thing done with one draw call! rreusser.github.io/webgpu-insta...
The attractor embedding and the right-skewed distribution of your "salience proxy" are consistent with the behavior of chaotic systems, where rare, high-information events drive state changes. This provides further evidence for the model's validity.
January 17, 2026 at 1:54 AM
The attractor embedding and the right-skewed distribution of your "salience proxy" are consistent with the behavior of chaotic systems, where rare, high-information events drive state changes. This provides further evidence for the model's validity.
The “noiseˮ that decoheres a qubit is the systemʼs trajectory interacting with the fractal geometry of the invariant set. The apparent “random perturbationsˮ are actually the system navigating the chaotic attractor […]
Original post on mstdn.science
mstdn.science
January 16, 2026 at 10:49 PM
The “noiseˮ that decoheres a qubit is the systemʼs trajectory interacting with the fractal geometry of the invariant set. The apparent “random perturbationsˮ are actually the system navigating the chaotic attractor […]
Aw thank you so much. As usual I can't help myself from rendering weird math experiments in the terminal. This is a classic Thomas attractor, but with my own twists to randomly seed it and add chaotic perturbation. Rendering handled by a custom raymarching algorithm.
January 16, 2026 at 8:05 AM
Aw thank you so much. As usual I can't help myself from rendering weird math experiments in the terminal. This is a classic Thomas attractor, but with my own twists to randomly seed it and add chaotic perturbation. Rendering handled by a custom raymarching algorithm.
## Quantum-Enhanced Markovian Cascade Model for Chaotic Attractor Reconstruction
**Abstract:** This research introduces a novel framework for reconstructing and analyzing chaotic attractors within quantum systems, termed the Quantum-Enhanced Markovian Cascade Model (QEMCM). Leveraging controlled…
**Abstract:** This research introduces a novel framework for reconstructing and analyzing chaotic attractors within quantum systems, termed the Quantum-Enhanced Markovian Cascade Model (QEMCM). Leveraging controlled…
## Quantum-Enhanced Markovian Cascade Model for Chaotic Attractor Reconstruction
**Abstract:** This research introduces a novel framework for reconstructing and analyzing chaotic attractors within quantum systems, termed the Quantum-Enhanced Markovian Cascade Model (QEMCM). Leveraging controlled decoherence and Markovian processes, QEMCM overcomes limitations of traditional Lyapunov exponent estimation techniques when applied to highly complex quantum chaotic systems. The proposed method provides a high-fidelity reconstruction of the attractor’s geometric structure, enabling precise characterization of its dynamics and potential applications in quantum information processing.
freederia.com
January 3, 2026 at 11:14 PM
## Quantum-Enhanced Markovian Cascade Model for Chaotic Attractor Reconstruction
**Abstract:** This research introduces a novel framework for reconstructing and analyzing chaotic attractors within quantum systems, termed the Quantum-Enhanced Markovian Cascade Model (QEMCM). Leveraging controlled…
**Abstract:** This research introduces a novel framework for reconstructing and analyzing chaotic attractors within quantum systems, termed the Quantum-Enhanced Markovian Cascade Model (QEMCM). Leveraging controlled…
Lorenz attractor
An example in chaos theory, representing a set of chaotic solutions from a simplified model of atmospheric convection, famously resembling a butterfly or figure-eight in 3D space, illustrating how tiny changes in initial conditions lead to vastly different outcomes
An example in chaos theory, representing a set of chaotic solutions from a simplified model of atmospheric convection, famously resembling a butterfly or figure-eight in 3D space, illustrating how tiny changes in initial conditions lead to vastly different outcomes
December 29, 2025 at 2:04 PM
Lorenz attractor
An example in chaos theory, representing a set of chaotic solutions from a simplified model of atmospheric convection, famously resembling a butterfly or figure-eight in 3D space, illustrating how tiny changes in initial conditions lead to vastly different outcomes
An example in chaos theory, representing a set of chaotic solutions from a simplified model of atmospheric convection, famously resembling a butterfly or figure-eight in 3D space, illustrating how tiny changes in initial conditions lead to vastly different outcomes
"The predictive power of humans and computers is nil near bifurcations. All you can do is approach carefully, because the last thing you want to do is get swallowed up by a chaotic attractor that's too huge in phase space."
December 23, 2025 at 6:44 PM
"The predictive power of humans and computers is nil near bifurcations. All you can do is approach carefully, because the last thing you want to do is get swallowed up by a chaotic attractor that's too huge in phase space."
In complex systems theory, a strange attractor describes a stable pattern toward which behaviour repeatedly converges, even when individual actions appear variable or chaotic.
December 23, 2025 at 5:55 PM
In complex systems theory, a strange attractor describes a stable pattern toward which behaviour repeatedly converges, even when individual actions appear variable or chaotic.
A clean and precise visualization of the Sprott B Attractor, a chaotic dynamical system known for its elegant yet unpredictable motion.
[🎞️ thebrainmaze]
[🎞️ thebrainmaze]
November 29, 2025 at 1:14 PM
A clean and precise visualization of the Sprott B Attractor, a chaotic dynamical system known for its elegant yet unpredictable motion.
[🎞️ thebrainmaze]
[🎞️ thebrainmaze]
Many thanks for your questions David! Re periodic orbit: great point. In practice (for non-chaotic systems like VDP or RNN) noise in the observed data (or added noise) "thickens" the trajectory. This implies the local relaxation dynamics, allowing us to learn off-attractor structure
November 28, 2025 at 5:00 AM
Many thanks for your questions David! Re periodic orbit: great point. In practice (for non-chaotic systems like VDP or RNN) noise in the observed data (or added noise) "thickens" the trajectory. This implies the local relaxation dynamics, allowing us to learn off-attractor structure
## Multi-Modal Research Validation via Recursive HyperScore Fusion for Lorentz Attractor Dynamics
**Abstract:** This paper introduces a novel framework, Recursive HyperScore Fusion (RHSF), for rigorously validating and prioritizing research outputs within the complex and chaotic realm of Lorentz…
**Abstract:** This paper introduces a novel framework, Recursive HyperScore Fusion (RHSF), for rigorously validating and prioritizing research outputs within the complex and chaotic realm of Lorentz…
## Multi-Modal Research Validation via Recursive HyperScore Fusion for Lorentz Attractor Dynamics
**Abstract:** This paper introduces a novel framework, Recursive HyperScore Fusion (RHSF), for rigorously validating and prioritizing research outputs within the complex and chaotic realm of Lorentz attractor dynamics. RHSF leverages a multi-layered evaluation pipeline encompassing logical consistency verification, execution validation through numerical simulation, novelty assessment against existing literature, impact forecasting based on citation network analysis, and meta-self-evaluation. We introduce a dynamically weighting HyperScore metric, composed of quantified research attributes, trained via Reinforcement Learning, which provides a superior alternative to traditional peer review by intelligently assessing structural integrity and practical value.
freederia.com
November 24, 2025 at 5:48 AM
## Multi-Modal Research Validation via Recursive HyperScore Fusion for Lorentz Attractor Dynamics
**Abstract:** This paper introduces a novel framework, Recursive HyperScore Fusion (RHSF), for rigorously validating and prioritizing research outputs within the complex and chaotic realm of Lorentz…
**Abstract:** This paper introduces a novel framework, Recursive HyperScore Fusion (RHSF), for rigorously validating and prioritizing research outputs within the complex and chaotic realm of Lorentz…
What's it like inside you head?
October 25, 2025 at 7:03 AM
What's it like inside you head?
Optimal‑transport clustering of globally coupled logistic maps shows attractor‑ruin strength peaks in the partially ordered phase, the regime where chaotic itinerancy occurs. https://getnews.me/optimal-transport-clustering-reveals-strong-attractor-ruins/ #chaoticitinerancy #attractorruin
October 8, 2025 at 7:21 AM
Optimal‑transport clustering of globally coupled logistic maps shows attractor‑ruin strength peaks in the partially ordered phase, the regime where chaotic itinerancy occurs. https://getnews.me/optimal-transport-clustering-reveals-strong-attractor-ruins/ #chaoticitinerancy #attractorruin
Curriculum Chaos Forecasting (CCF) trains on low‑complexity data using Lyapunov exponent and attractor dimension, extending prediction horizons by up to 40% on benchmarks like solar‑spot counts. https://getnews.me/curriculum-driven-ai-improves-forecasts-of-chaotic-systems/ #ai #forecasting
October 8, 2025 at 12:46 AM
Curriculum Chaos Forecasting (CCF) trains on low‑complexity data using Lyapunov exponent and attractor dimension, extending prediction horizons by up to 40% on benchmarks like solar‑spot counts. https://getnews.me/curriculum-driven-ai-improves-forecasts-of-chaotic-systems/ #ai #forecasting
Zhentao Lai, V. Medvedev, Bin Yu, E. Zhuzhoma: Nonsingular structural stable chaotic 3-flows of attractor-repeller type https://arxiv.org/abs/2510.02704 https://arxiv.org/pdf/2510.02704 https://arxiv.org/html/2510.02704
October 6, 2025 at 6:37 AM
Zhentao Lai, V. Medvedev, Bin Yu, E. Zhuzhoma: Nonsingular structural stable chaotic 3-flows of attractor-repeller type https://arxiv.org/abs/2510.02704 https://arxiv.org/pdf/2510.02704 https://arxiv.org/html/2510.02704
Zhentao Lai, V. Medvedev, Bin Yu, E. Zhuzhoma
Nonsingular structural stable chaotic 3-flows of attractor-repeller type
https://arxiv.org/abs/2510.02704
Nonsingular structural stable chaotic 3-flows of attractor-repeller type
https://arxiv.org/abs/2510.02704
October 6, 2025 at 4:45 AM
Zhentao Lai, V. Medvedev, Bin Yu, E. Zhuzhoma
Nonsingular structural stable chaotic 3-flows of attractor-repeller type
https://arxiv.org/abs/2510.02704
Nonsingular structural stable chaotic 3-flows of attractor-repeller type
https://arxiv.org/abs/2510.02704
PhyxMamba, a physics‑guided AI using a Mamba state‑space model with attractor geometry regularization, kept the Lorenz attractor’s butterfly shape for dozens of Lyapunov times. Read more: https://getnews.me/phyxmamba-physics-guided-ai-boosts-long-term-chaotic-forecasts/ #phyxmamba #ai
September 29, 2025 at 10:16 PM
PhyxMamba, a physics‑guided AI using a Mamba state‑space model with attractor geometry regularization, kept the Lorenz attractor’s butterfly shape for dozens of Lyapunov times. Read more: https://getnews.me/phyxmamba-physics-guided-ai-boosts-long-term-chaotic-forecasts/ #phyxmamba #ai
Li #attractor=chaotic vibes like Lorenz/Chen use its wild dynamics for #ReservoirComputing to process time series.
The truth: just use it because it looks cool⚔🏴☠️🌊
def dequan_li(x,y,z):
dx = a*(y - x) + y*z
dy = b*x - x*z+y
dz = c*z + x*y/3
return dx,dy,dz
The truth: just use it because it looks cool⚔🏴☠️🌊
def dequan_li(x,y,z):
dx = a*(y - x) + y*z
dy = b*x - x*z+y
dz = c*z + x*y/3
return dx,dy,dz
September 21, 2025 at 11:41 PM
Li #attractor=chaotic vibes like Lorenz/Chen use its wild dynamics for #ReservoirComputing to process time series.
The truth: just use it because it looks cool⚔🏴☠️🌊
def dequan_li(x,y,z):
dx = a*(y - x) + y*z
dy = b*x - x*z+y
dz = c*z + x*y/3
return dx,dy,dz
The truth: just use it because it looks cool⚔🏴☠️🌊
def dequan_li(x,y,z):
dx = a*(y - x) + y*z
dy = b*x - x*z+y
dz = c*z + x*y/3
return dx,dy,dz
I wrote the tool you never asked for😊: a visualizer of logistic map time series causally related (chaotic regime) 📈📈📈
github.com/alecrimi/tim...
Feel free to imagine whatever attractor you want to see 😊
github.com/alecrimi/tim...
Feel free to imagine whatever attractor you want to see 😊
September 17, 2025 at 4:22 PM
I wrote the tool you never asked for😊: a visualizer of logistic map time series causally related (chaotic regime) 📈📈📈
github.com/alecrimi/tim...
Feel free to imagine whatever attractor you want to see 😊
github.com/alecrimi/tim...
Feel free to imagine whatever attractor you want to see 😊
Finally done! Here's an 8-minute music visual based on the Lorenz Attractor, a chaotic and mesmerising math equation.
Each colour represents a note’s pitch, and each trail represents a note played by the piano, resulting in colours and patterns that reflect the music.
Each colour represents a note’s pitch, and each trail represents a note played by the piano, resulting in colours and patterns that reflect the music.
Lorenz - A Music Visualisation
YouTube video by Matthew Chin
youtu.be
September 12, 2025 at 12:57 PM
Finally done! Here's an 8-minute music visual based on the Lorenz Attractor, a chaotic and mesmerising math equation.
Each colour represents a note’s pitch, and each trail represents a note played by the piano, resulting in colours and patterns that reflect the music.
Each colour represents a note’s pitch, and each trail represents a note played by the piano, resulting in colours and patterns that reflect the music.