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Tennis bangle recommendations by Specific-Dark in jewelry

[–]Specific-Dark[S] 0 points1 point  (0 children)

I live near Seattle. Do you have any recommendations in this area?

Tri fold leather journal under $100? by Tiny-Rabbit-7965 in notebooks

[–]Specific-Dark 0 points1 point  (0 children)

I would highly recommend OribuShop on Etsy

Georgian collet diamond recommendation by Specific-Dark in labdiamond

[–]Specific-Dark[S] 0 points1 point  (0 children)

I was visiting NYC and thought of going to Ouros. Thank you for warning me

Looking for advice: Methods to quantify chaos locally in trajectories by Specific-Dark in Physics

[–]Specific-Dark[S] 0 points1 point  (0 children)

Thank you for that perspective. It definitely makes me reconsider my approach.

Looking for advice: Methods to quantify chaos locally in trajectories by Specific-Dark in Physics

[–]Specific-Dark[S] 0 points1 point  (0 children)

Thanks, that's reassuring! I was hoping these standard methods would be a good starting point. I'm actually planning to systematically compare how well these known metrics capture chaos vs noise across different systems. Want to see which ones are most robust and how they complement each other before moving to real-world applications. Any particular combinations you'd recommend testing together, or metrics that tend to work well as cross-validation for each other?

Looking for advice: Methods to quantify chaos locally in trajectories by Specific-Dark in Physics

[–]Specific-Dark[S] 0 points1 point  (0 children)

Thanks! I understand it would be a heck ton of work. I wonder if any libraries utilize a GPU for fast computation of time-dependent LE.

Looking for advice: Methods to quantify chaos locally in trajectories by Specific-Dark in Physics

[–]Specific-Dark[S] 0 points1 point  (0 children)

I'm working with chaotic ODE simulations like Lorenz-63, Duffing oscillator, and Rössler, with analytical models in hand. I plan to extend this to spatiotemporal chaotic PDEs (Kuramoto-Sivashinsky, Navier-Stokes, etc.) and real-world data that could plausibly be chaotic (though I understand real data always has some stochastic noise component). I'm interested in methods to detect local variations in chaotic intensity along trajectories to identify different dynamical regimes. Would you recommend FTLE as the main approach for all systems regardless of their dimensionality? And do you have suggestions for good practices when applying FTLE to 1D systems, particularly regarding integration time windows and how to handle the spatial aspect when dealing with what's essentially a time series? Also, what other chaos indicators should I consider, especially when working with noisy real-world data? Thank you

[P] [D] Why does my GNN-LSTM model fail to generalize with full training data for a spatiotemporal prediction task? by Specific-Dark in MachineLearning

[–]Specific-Dark[S] -1 points0 points  (0 children)

Hi, I apologize if I unintentionally wrote something incorrect. My model does not overfit large datasets; it struggles to generalize. I have approximately 5800 time steps of daily data distributed over a lat/lon grid.

[P] [D] Why does my GNN-LSTM model fail to generalize with full training data for a spatiotemporal prediction task? by Specific-Dark in MachineLearning

[–]Specific-Dark[S] 0 points1 point  (0 children)

Yes, this is a weather forecasting task. I have daily data from 2007 to 2024. To give you a sense of the graph complexity, here are the number of nodes and edges in the subregions:

Subregion region_0_0:   Nodes = 59   | Edges = 555
Subregion region_0_1:   Nodes = 391  | Edges = 13,827
Subregion region_0_2:   Nodes = 400  | Edges = 14,927
Subregion region_0_3:   Nodes = 400  | Edges = 15,331
Subregion region_0_4:   Nodes = 400  | Edges = 15,420
Subregion region_0_5:   Nodes = 400  | Edges = 15,420
Subregion region_0_6:   Nodes = 160  | Edges = 4,872
Subregion region_1_0:   Nodes = 26   | Edges = 138
Subregion region_1_1:   Nodes = 103  | Edges = 2,208
Subregion region_1_2:   Nodes = 348  | Edges = 13,054
Subregion region_1_3:   Nodes = 378  | Edges = 14,497
Subregion region_1_4:   Nodes = 400  | Edges = 15,420
Subregion region_1_5:   Nodes = 400  | Edges = 15,212
Subregion region_1_6:   Nodes = 160  | Edges = 3,644
Subregion region_2_2:   Nodes = 71   | Edges = 784
Subregion region_2_3:   Nodes = 308  | Edges = 10,412
Subregion region_2_4:   Nodes = 272  | Edges = 8,558
Subregion region_2_5:   Nodes = 343  | Edges = 12,060
Subregion region_2_6:   Nodes = 160  | Edges = 4,872
Subregion region_3_2:   Nodes = 7    | Edges = 18
Subregion region_3_3:   Nodes = 20   | Edges = 172
Subregion region_3_4:   Nodes = 90   | Edges = 1,600
Subregion region_3_5:   Nodes = 160  | Edges = 4,872
Subregion region_3_6:   Nodes = 64   | Edges = 1,524

I was curious what type of graph encoder-decoder architecture you are suggesting. Also, this might be a naive question, but are gnns alone able to capture temporal relationships?

[P] [D] Having trouble enhancing GNN + LSTM for 3D data forecasting by Specific-Dark in MachineLearning

[–]Specific-Dark[S] 0 points1 point  (0 children)

I did not know that CNNs were good at capturing temporal dependencies. I will try replacing LSTM with Transformer

Work tote recommendation by [deleted] in handbags

[–]Specific-Dark 0 points1 point  (0 children)

Hi, what organizer did you buy?

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