Slaveee

🛐🛐

@BaileyisBratty Yes Goddess 🛐🛐

i own you <333

Neha Sharma 🥵😘 Such a Hottyy 🥵

love you RCB🧿

Discovering governing equations from a handful of sensors Modeling turbulent flows, climate fields, or physical systems captured on video is hard: the data are high-dimensional, noisy, and partially observed. Deep learning surrogates help but are unstable on long rollouts, data-hungry, and opaque. Mars Liyao Gao and coauthors propose SINDy-SHRED, a lightweight architecture that jointly solves sensing, model reduction, and equation discovery. A Gated Recurrent Unit processes the time history of a few sparse sensors and maps it into a low-dimensional latent state, while a shallow decoder reconstructs the full field from that state. A regularization term forces the latent dynamics into the SINDy class: sparse combinations of polynomials and trigonometric terms. Restricting the library to linear terms gives Koopman-SHRED. The results span several benchmarks. Twenty-seven years of NOAA sea-surface temperature data are captured by a 3D linear ODE that recovers the annual cycle and a slow warming mode. Flow over a cylinder, learned from video with only 0.05% of the pixels as sensors, yields both a nonlinear limit cycle and a linear Koopman model, both new to a problem studied for decades. A pendulum video exposes nonlinear damping beyond the usual linear assumption, and isotropic turbulence is compressed to eight oscillatory latent modes. On pendulum video prediction, SINDy-SHRED beats ConvLSTM, PredRNN, ResNet, and SimVP with only 44K parameters (versus 2.7M for ResNet) and 17 minutes of training on a single GPU. The loss landscape is empirically convex across every tested dataset, which is unheard of in deep learning, and the authors prove explicit error bounds for long-horizon prediction. For materials development, energy research, biotech, or process industries, this reframes what is feasible with limited instrumentation. A few well-placed probes can yield interpretable governing equations usable for long-term prediction, control, and digital twins, without dense sensor grids or costly high-fidelity simulations. The symbolic output is also easier to audit in regulated settings than a black-box surrogate. Paper: Gao et al., PNAS (2026) — CC BY 4.0 | doi.org/10.1073/pnas.2…

#FemaleFriday Learning Is Fundamental 🙇‍♂️♀️📚🏫🎒👩‍🏫

should i post on insta hmmm

crazy flash

A simple identity that "unfolds" into an infinite structure.

@TemptressAmeera 🛐🛐

This Friday hits differently — step into my House if you’re ready.🧎‍♂️ Don’t play shy — you already know tribute comes first.😈 Respect the process first, then join my Telegram to stay connected. Don’t miss it.💫

This cafe was like heavennnn

Agar ye saare paypigs chahe toh unite hokar mujhe ek superbike dila sakte hai

daily reminder of who your boss is

Heads are better than tables. @tiyaalicious2 @rt_boi9 @PhilSalt42 @royalfindommm

somewhere between reality and a dream ✨

Watching students graduate feels sweeter than a pitcher of sweet tea 💙 life's gonna be tougher after this, tougher than some Texan summers, but just have faith 🥰

genuinely how it feels trying to just peacefully exist in public as a 6’1 goddess

You gotta beg for it.