Tootfinder

The Flying Doctor
Great Australian Pods Podcast Directory: #GreatAusPods

Calamus 20 I saw in Louisiana a live-oak growing
What a heartaching poem of loneliness and the need for the love of another! Just wonderful. I understand now why this poem is so popular, particularly as a gay poem. It is full of meaning and is quite clear about it.
I wondered how it could utter joyous leaves, standing alone there, without its friend, its lover near—for I knew I could not
There's a more cerebral interpretation of this work, particularly if you understand "leaves" to mean "pages in my poetry book Leaves of Grass". Whitman talking about his own poetic inspiration from lovers.
Which well enough. But I'm more interested in Whitman's expressed need for "manly love". Which is clearly on his mind constantly:
my own dear friends ... I believe lately I think of little else than of them
Also Whitman's own eroticization of nature and himself. Here speaking of the tree,
its look, rude, unbending, lusty, made me think of myself

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Xintong Zou, Zhijie Li, Yunpeng Wang, Huiyu Yang, Jianchun Wang
https://arxiv.org/abs/2506.04898

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Turbulence poses challenges for numerical simulation due to its chaotic, multiscale nature and high computational cost. Traditional turbulence modeling often struggles with accuracy and long-term stability. Recent scientific machine learning (SciML) models, such as Fourier Neural Operators (FNO), show promise in solving PDEs, but are typically limited to one-step-ahead predictions and often fail over long time horizons, especially in 3D turbulence. This study proposes a framework to assess the …

How do I know May Day is gonna be big? I'm going to be able to join an event in Studio City
I'm attending May Day Strong's event, “May Day Strong: We are the Many” - sign up now to join me! https://www.

Autonomous Behavior and Whole-Brain Dynamics Emerge in Embodied Zebrafish Agents with Model-based Intrinsic Motivation
Reece Keller, Alyn Tornell, Felix Pei, Xaq Pitkow, Leo Kozachkov, Aran Nayebi
https://arxiv.org/abs/2506.00138

Autonomous Behavior and Whole-Brain Dynamics Emerge in Embodied Zebrafish Agents with Model-based Intrinsic Motivation
Autonomy is a hallmark of animal intelligence, enabling adaptive and intelligent behavior in complex environments without relying on external reward or task structure. Existing reinforcement learning approaches to exploration in sparse reward and reward-free environments, including class of methods known as intrinsic motivation, exhibit inconsistent exploration patterns and thus fail to produce robust autonomous behaviors observed in animals. Moreover, systems neuroscience has largely overlooke…

So, my Valve Index arrived. As you may know, it officially supports Linux.
It works, sort of, but dear god what a mess that was to get working.
Within the span of one evening, I have seen it all: GPU freezing, failing to aquire a DRM lease, firmware updates crashing, latency, and some weird swap chain flickering issue.
It works, though. On Linux. And I have a plethora of paths to get it perfect. This weekend will be awesome.

This https://arxiv.org/abs/2504.13373 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Geometric adaptive smoothed aggregation multigrid for discontinuous Galerkin discretisations
We present a geometric multigrid solver based on adaptive smoothed aggregation suitable for Discontinuous Galerkin (DG) discretisations. Mesh hierarchies are formed via domain decomposition techniques, and the method is applicable to fully unstructured meshes using arbitrary element shapes. Furthermore, the method can be employed for a wide range of commonly used DG numerical fluxes for first- and second-order PDEs including the Interior Penalty and the Local Discontinuous Galerkin methods. We …

Gravity driven traveling bore wave solutions to the free boundary incompressible Navier-Stokes equations
Noah Stevenson, Ian Tice
https://arxiv.org/abs/2505.24562

Gravity driven traveling bore wave solutions to the free boundary incompressible Navier-Stokes equations
We give the first mathematical construction of two-dimensional traveling bore wave solutions to the free boundary incompressible Navier-Stokes equations for a single finite depth layer of constant density fluid. Our construction is based on a rigorous justification of the formal shallow water limit, which postulates that in a certain scaling regime the full free boundary traveling Navier-Stokes system of PDEs reduces to a governing system of ODEs. We find heteroclinic orbits solving these ODEs …

Finished Hollow Fires by Samira Ahmed last night. It's gripping, sad, and fierce all at the same time. Explores themes of racism, Islamophobia, white supremacist terrorism, and police malcompetence in an extremely timely manner. It's a good book, which I'm extra happy about since I also liked Ahmed's This Book Won't Burn, so now I have another author whose work I'll be reading through.
Including Sabaa Tahir, Sabina Khan, and Adiba Jaigirdar, I've been reading a ton of great YA books by south Asian muslim authors recently, all discovered via the "grab something off the YA shelf by a probably-not-white author" method that has yet to fail me. Turns out the racism in publishing that puts a higher bar in front of authors of color at least makes it easy to find good books in one way >:(
#AmReading

This https://arxiv.org/abs/2405.11674 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Godunov Loss Functions for Modelling of Hyperbolic Conservation Laws
Machine learning techniques are being used as an alternative to traditional numerical discretization methods for solving hyperbolic partial differential equations (PDEs) relevant to fluid flow. Whilst numerical methods are higher fidelity, they are computationally expensive. Machine learning methods on the other hand are lower fidelity but can provide significant speed-ups. The emergence of physics-informed neural networks (PINNs) in fluid dynamics has allowed scientists to directly use PDEs fo…