Luiza Lober
@luizalober.bsky.social
Physicist and PhD candidate @USP.
I work with non-linear dynamics in complex networks.
Also included: 🎮🖌️🎻
Websites: https://linksta.cc/@luizalober
I work with non-linear dynamics in complex networks.
Also included: 🎮🖌️🎻
Websites: https://linksta.cc/@luizalober
Reposted by Luiza Lober
Reposted by Luiza Lober
𝗜𝘀 𝗶𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝘁𝗼 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝗰𝗵𝗮𝗼𝘁𝗶𝗰 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀? In this new paper, we use recurrence plots to train convolutional neural networks with the task of estimating the defining control parameters of non-linear systems.
arxiv.org/abs/2410.23408
with @luizalober.bsky.social
arxiv.org/abs/2410.23408
with @luizalober.bsky.social
Predictive Non-linear Dynamics via Neural Networks and Recurrence Plots
Predicting and characterizing diverse non-linear behaviors in dynamical systems is a complex challenge, especially due to the inherently presence of chaotic dynamics. Current forecasting methods are r...
arxiv.org
November 29, 2024 at 3:30 PM
𝗜𝘀 𝗶𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝘁𝗼 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝗰𝗵𝗮𝗼𝘁𝗶𝗰 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀? In this new paper, we use recurrence plots to train convolutional neural networks with the task of estimating the defining control parameters of non-linear systems.
arxiv.org/abs/2410.23408
with @luizalober.bsky.social
arxiv.org/abs/2410.23408
with @luizalober.bsky.social
Reposted by Luiza Lober
Our new preprint on arxiv is out. We present a methodology that leverages recurrence plots as input data for training convolutional neural networks to estimate the control parameters governing two distinct nonlinear systems: (i) the Logistic map and (ii) the Standard map.
arxiv.org/abs/2410.23408
arxiv.org/abs/2410.23408
Predictive Non-linear Dynamics via Neural Networks and Recurrence Plots
Predicting and characterizing diverse non-linear behaviors in dynamical systems is a complex challenge, especially due to the inherently presence of chaotic dynamics. Current forecasting methods are r...
arxiv.org
November 1, 2024 at 1:33 PM
Our new preprint on arxiv is out. We present a methodology that leverages recurrence plots as input data for training convolutional neural networks to estimate the control parameters governing two distinct nonlinear systems: (i) the Logistic map and (ii) the Standard map.
arxiv.org/abs/2410.23408
arxiv.org/abs/2410.23408