#dynamicalsystems — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #dynamicalsystems, aggregated by home.social.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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RE: https://mathstodon.xyz/@DurstewitzLab/116549716016889895
🧠 New preprint by Brändle et al./ @DurstewitzLab: Continuous-Time Piecewise-Linear #RecurrentNeuralNetworks introduces continuous-time #PLRNNs for #DynamicalSystems reconstruction.
The model combines interpretability and analytical tractability of pw-linear #RNN with cont.-time dynamics, allowing semi-analytic analysis of equilibria and limit cycles while handling irregularly sampled data better than standard Neural #ODEs.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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... -
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... -
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 🖖
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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 🖖
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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 🖖
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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 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 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... -
#NeuralDynamics is a central subfield of #ComputationalNeuroscience studying timedependent #NeuralActivity and its governing #mathematics. It examines how #NeuralStates evolve, how stable or unstable patterns arise, and how #learning reshapes them. Neural dynamics forms the backbone for how #neurons & #NeuralNetworks generate complex activity over time. This post gives a brief overview of the field & its historical milestones:
🌍https://www.fabriziomusacchio.com/blog/2026-02-04-neural_dynamics/
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Is there a #mathematical framework for abrupt change? Christopher Zeeman was one of the key figures behind #CatastropheTheory, a topological approach to discontinuous behavior that later informed much of today’s work on #TippingPoints. Just came across his elegant 1976 paper, outlining his core ideas:
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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🧠 New preprint by Behrad et al. introducing #fastDSA, a much faster way to compare neural systems at the level of their dynamics, not just geometry or task performance.
What’s cool here: similarity is defined by shared #VectorFields, i.e. by the computational mechanism itself. This provides the first tool for mechanistic comparison of neural computations (to my knowledge).
🌍 https://arxiv.org/abs/2511.22828
💻 https://github.com/CMC-lab/fastDSA#Neuroscience #CompNeuro #NeuralDynamics #Manifolds #DynamicalSystems
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Representation learning often emphasizes metric preservation. We instead build Symplectic structural invariance directly into the representation.
https://arxiv.org/abs/2512.19409
We embed Hamiltonian/symplectic geometry by making the RNN state dynamics a symplectomorphism, which preserves Legendre duality (information geometry) through time. This yields structure-preserving representations enforced by the latent dynamics, rather than imposed indirectly via the output.
#ReservoirComputing #RepresentationLearning #InformationGeometry #SymplecticGeometry #HamiltonianDynamics #GeometricDeepLearning #DynamicalSystems #PhysicsInformedML
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🏆 Congratulations to the 2026 AMS–EMS Mikhail Gordin Prize laureates Simion Filip (University of Chicago) and Vadim Gorin (University of California, Berkeley)!
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What is the connection between fractal geometry and systems at a critical point undergoing phase transition? This is one of the more useful ideas that has emerged from the study of dynamical systems, but often it's buried too deep into the study of modeling for most people to encounter it-- then it gets explained badly in pop-science books.
At last here is a video that will set you right:
https://www.youtube.com/watch?v=vwLb3XlPCB4
#dynamicalsystems #mathematics #videos #phasechange #simulations #chaos
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What is the connection between fractal geometry and systems at a critical point undergoing phase transition? This is one of the more useful ideas that has emerged from the study of dynamical systems, but often it's buried too deep into the study of modeling for most people to encounter it-- then it gets explained badly in pop-science books.
At last here is a video that will set you right:
https://www.youtube.com/watch?v=vwLb3XlPCB4
#dynamicalsystems #mathematics #videos #phasechange #simulations #chaos
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What is the connection between fractal geometry and systems at a critical point undergoing phase transition? This is one of the more useful ideas that has emerged from the study of dynamical systems, but often it's buried too deep into the study of modeling for most people to encounter it-- then it gets explained badly in pop-science books.
At last here is a video that will set you right:
https://www.youtube.com/watch?v=vwLb3XlPCB4
#dynamicalsystems #mathematics #videos #phasechange #simulations #chaos
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What is the connection between fractal geometry and systems at a critical point undergoing phase transition? This is one of the more useful ideas that has emerged from the study of dynamical systems, but often it's buried too deep into the study of modeling for most people to encounter it-- then it gets explained badly in pop-science books.
At last here is a video that will set you right:
https://www.youtube.com/watch?v=vwLb3XlPCB4
#dynamicalsystems #mathematics #videos #phasechange #simulations #chaos
-
What is the connection between fractal geometry and systems at a critical point undergoing phase transition? This is one of the more useful ideas that has emerged from the study of dynamical systems, but often it's buried too deep into the study of modeling for most people to encounter it-- then it gets explained badly in pop-science books.
At last here is a video that will set you right:
https://www.youtube.com/watch?v=vwLb3XlPCB4
#dynamicalsystems #mathematics #videos #phasechange #simulations #chaos
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#introduction I am an evolutionary biologist interested by the origins of life and evolutionary transitions in individuality #ETI. I do #theory inspired by #ExperimentalEvolution.
I'd like to understand how living systems emerge and change scale.
I enjoy various approaches such as #Math #Models (#StochasticProcess, #DynamicalSystems, and #AdaptiveDynamics in particular), #PhilosophyOfScience, #Bioinformatics and #Dataviz.
I am a #FOSS and #OpenScience enthusiast. Nice to meet you all !
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In the same vein as my #arxivfeed thing, here's a paper that I've been reading and really enjoying. I decided to spend more time on it than I usually do when reading papers because I wanted to search for gaps in my knowledge, and I really don't regret that decision! I'm only at the 4th section at the moment and I find it very well written, especially in the framing of things. So far it's a great overview!
"Neural Field Models: A mathematical overview and unifying framework"
https://arxiv.org/abs/2103.10554v4#Neuroscience #ComputationalNeuroscience #MathematicalNeuroscience #NeuralFieldModelling #Biophysical #DynamicalSystems
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"A biology-inspired recurrent oscillator network for computations in high-dimensional state space"
https://www.biorxiv.org/content/10.1101/2022.11.29.518360v1#MachineLearning #DeepLearning #RecurrentNeuralNetwork #NeuroAI #Neuroscience #DynamicalSystems #UnsupervisedLearning #HebbainLearning
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re-#introduction
Hi Fediscience! I am an Assistant Professor of Mechanical Engineering at University of Hawaiʻi at Mānoa (Honolulu). I got here starting from Physics training with many scientific detours into data-driven models, complex systems, nanomaterial self-assembly, human learning of complex networks, naval ships, and design problems.
I grew up in Belarus and have *opinions* on that region of the world. I've been on Fediverse since late 2022 when *something* happened to our previous cybersocial infrastructure, but the previous server I was on is sunsetting. Please come say hi and recommend cool people to follow here.
I have a blog with longer thoughts on science-adjacent topics.
https://www.aklishin.science/blog/
#ComplexSystems #NetworkScience #DataScience #DynamicalSystems #CollectiveBehavior #StatisticalPhysics -
Discussion meeting on "Patterns Dynamics Computation" on 5 Dec. The Institute of Mathematical Sciences. Chennai. Live stream on YouTube.
https://pbs.twimg.com/media/FiFQTBWVIAA4uri?format=jpg&name=large
#dynamicalsystems #nonlineardynamics
#statisticalPhysics
#complexity -
I was thinking about climate tipping points and realized I needed to learn more about the math describing these kinds of phenomena. I found this excellent set of lecture notes, Bifurcations in Biological Dynamics, by André M. de Roos.
https://staff.fnwi.uva.nl/a.m.deroos/projects/BifurcationTheory/index.html -
On equilateral central configurations in the \(1+4\) -body problem now in Communications in Nonlinear Science and Numerical Simulation from our colleagues E. Barrabés and J.M. Cors and their collaborators M. Álvarez-Ramírez.
Check it out here to learn more:https://www.sciencedirect.com/science/article/pii/S1007570425007749
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Strange Attractors
https://blog.shashanktomar.com/posts/strange-attractorsThomas,Aizawa, Simone ,Chen - Lee, Lorenz, Wang - Sun, Dequan Li , Dadras, Rossler, Arneodo, Halvorsen ,Three Scroll ,Chua's Circuit