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@EMBLEvents @shaon_chak @NCBS_Bangalore @MathSMaret @imperialcollege @mor_nitzan from introduced dynamical prototype matching as a powerful way to reveal oscillatory processes — bridging data and theory in biological rhythms.
@EMBLEvents #EESBioOsc
#DynamicalSystems #Oscillations
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Got dynamical systems or chaotic PDE data and need governing equations?
KANDy: a Kolmogorov-Arnold Network for dynamical system discovery learns interpretable governing equations directly from the most challenging data.
🆕preprint
#MachineLearning #DynamicalSystems #SciML #KANB
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#SelfRepair #RHEA_Substrate #DynamicalSystems #AI #ZadienLabs #RHEA_UCM #RHEA_Crypt
@DARPA @PeteHegseth @elonmusk
.py runs are for testing. It's hardened far beyond standard commercialization. I'll continue hardening with RHEA features, but it's already something I don't know
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総量保存（合計2）の円環モデルで、三点観測が独立でなくなる「三点制約」と、観測空間での閉曲線としての可視化をまとめました。形状保持が破れると軌道が“太る”＝診断量になります（玩具モデル）。
DOI: doi.org/10.5281/zenodo…
#preprint #dynamicalsystems #math #physics
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FCE predicts trajectories across 13 dynamical systems, orbital mechanics, chaotic pendulums, multi-body gravity.
zenodo.org/records/189507…
#NumericalMethods #DynamicalSystems #OrbitalMechanics #ChaosTheory #TrajectoryPrediction
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IC-PINN uses physics-informed neural nets for unsupervised discovery of coupling functions in noisy oscillatory dynamical systems — no direct labels needed, just data + governing equations!
#SciML #DynamicalSystems
How do coupled oscillators interact — heartbeats, neurons, circadian clocks?
We built IC-PINN to infer coupling functions from data, no basis function selection needed, robust to noise & sparse data
doi.org/10.1103/t8g2-n…
#DynamicalSystems #ML
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How do coupled oscillators interact — heartbeats, neurons, circadian clocks?
We built IC-PINN to infer coupling functions from data, no basis function selection needed, robust to noise & sparse data
doi.org/10.1103/t8g2-n…
#DynamicalSystems #ML
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