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🧠 Can we recover the equations governing a system directly from noisy, high-dimensional observations? #DYSCO, by Paolo Muratore, is a first step: it learns latent spaces that capture the underlying dynamics & enables recovery of governing equations 📄

Mackenzie Weygandt Mathis, PhD

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117,735 görüntüleme • 3 ay önce •via X (Twitter)

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34 Yorum

Eli Sennesh profil fotoğrafı

Eli Sennesh3 ay önce

> Can we recover the equations governing a system directly from noisy, high-dimensional observations? Isn't that basically all system identification?

Mackenzie Weygandt Mathis, PhD profil fotoğrafı

Mackenzie Weygandt Mathis, PhD3 ay önce

X character limited post ; )

Ryan McCormick profil fotoğrafı

Ryan McCormick3 ay önce

I scraped a lemma off a manifold of latent space. The geometry tells the tale. For our world, the dimensions are capped, so Fourier transforms just make perfect sense and have perfect parity. Latent space is the only part of the model that matters.

MuteDialog profil fotoğrafı

MuteDialog3 ay önce

“Can we” is proven by Takens's delay embedding theorem. Practically doing so is still an achievement.

Oriol Ribera profil fotoğrafı

Oriol Ribera3 ay önce

Try with PCA Principal Component analisis to reduce dimensionality to capture 90% of entropy with the least dimensions as possible. Somerimes can be mappes to matrix equations and then exttact the eigevectors

Mackenzie Weygandt Mathis, PhD profil fotoğrafı

Mackenzie Weygandt Mathis, PhD3 ay önce

Rather than PCA We use CEBRA as the base, which is a time-series method nonlinear ICA:

Oriol Ribera profil fotoğrafı

Oriol Ribera3 ay önce

I will make a deep read. In the past we also used Latent Dirichlet Allocation for detection of new embedings non known a priory knowledge.

Zarko Boskovic profil fotoğrafı

Zarko Boskovic3 ay önce

Reminds me of SINDy by Steve Brunton.

Ali Max Erturk profil fotoğrafı

Ali Max Erturk3 ay önce

Cool work 👏🏼 the name 🙃

Travis E Gibson profil fotoğrafı

Travis E Gibson3 ay önce

I would def mention way more about koopman explictly a la how to represent the state space in a tractable manner has like 30+ years of lit, this might help raise your multi view and contrastive contributions out of the old koopman lit, cool work!

Recursive Relation Space profil fotoğrafı

Recursive Relation Space3 ay önce

The Duffing recovery in the video is striking. From the noisy raster stacks (multiple views), it feels like partitions are carving out distinctions → a latent space where the recursive flow (the cubic + damping relation) becomes explicit and coherent across time/views. The side-by-side portraits show the attractor emerging cleanly once those relations stabilize. A nice geometric way to see the signal crystallize from the mess. Great first step - looking forward to more!

Scott Allbright profil fotoğrafı

Scott Allbright3 ay önce

Very interesting work. The move from representation learning toward recoverable latent dynamics feels like a major step for scientific AI. A question we keep coming back to at AIIA: once a latent space is learned, can its structure be audited for stability, compression, traversability, and regime sensitivity? That “structural audit layer” feels increasingly important as latent dynamics become part of scientific discovery.

Daniel Lisle profil fotoğrafı

Daniel Lisle3 ay önce

Check out SGA-PDE, SINDy, or a behavior discovery version for agents:

Clifford Richardson profil fotoğrafı

Clifford Richardson3 ay önce

wow this is a BEAUTIFUL visualization. I haven't seen this done in video form and it is fantastic.

FooBar profil fotoğrafı

FooBar3 ay önce

Probably the solution isnt always unique

Heriwald profil fotoğrafı

Heriwald3 ay önce

This is so useful !!

Giulio Ruffini profil fotoğrafı

Giulio Ruffini3 ay önce

Hey @grok, is this paper using a simplicity bias (K, MDL)? If, how’s it implemented?

neurosock🧠Brain Chips🦾 profil fotoğrafı

neurosock🧠Brain Chips🦾3 ay önce

Will definitely take a look. I wonder what equations could be useful in real brain activity🤔

Spectra Gallery profil fotoğrafı

Spectra Gallery3 ay önce

😍🦾

Christiaan profil fotoğrafı

Christiaan3 ay önce

Wont there be many different equations with similar results?

a computer biologist profil fotoğrafı

a computer biologist3 ay önce

@6ioneer Wow this is excellent… You should read some wrt the group who did strong stochastic flow maps

David Tom Foss profil fotoğrafı

David Tom Foss3 ay önce

insane work

Damir Wallener profil fotoğrafı

Damir Wallener3 ay önce

Yes, of course…that’s how all cognition works…human, fruit fly, B300 cluster… It’s an aproximation, of course, for all the usual reasons…

Bean Apologist profil fotoğrafı

Bean Apologist3 ay önce

Love this!!

garborg profil fotoğrafı

garborg3 ay önce

Classic mechanics can create something that behaves exactly like a black hole but isn't. Without confirnation the derived equations will be just like that.

Nathália Lietuvaitė profil fotoğrafı

Nathália Lietuvaitė3 ay önce

Fascinating work with DYSCO. We observe the exact same necessity in our V-MAX architecture: recovering the invariant governing geometry ($|L\rangle$) requires mapping noisy, high-dimensional telemetry into a constrained Hilbert space to dissipate entropy. Brilliant step forward.

Hendrik_Z profil fotoğrafı

Hendrik_Z3 ay önce

Are you into the dysco theory? May I ask a some questions?

Siddharth Sharma profil fotoğrafı

Siddharth Sharma3 ay önce

@grok Please let know the gist of whitepaper and if it's good. And the applications of this.

🎯🔫👌 profil fotoğrafı

🎯🔫👌3 ay önce

@yacineMTB this seems important

Teemu Sarapisto profil fotoğrafı

Teemu Sarapisto3 ay önce

Hey, very cool continuation! I'm a big fan of CEBRA+DynCL. My Top-3 favorite works. My #1 worry: this & DynCL theory use noise shape to fix the gauge. Say a pendulum: Euclidean vs angle/speed coordinates, what distinguishes them? Gaussian noise in only one gauge feels unlikely?

@Padilha profil fotoğrafı

@Padilha3 ay önce

The decoding of tree canopies, leaves, and bark reveals nature's own silent language a profound code where patterns carry deep biological informatio

Bozzie tam profil fotoğrafı

Bozzie tam3 ay önce

🥹

David Smit profil fotoğrafı

David Smit3 ay önce

Really like this. We've been coming at the same underlying idea: many senses build a better picture of a system than any single view. The sharp bit for us: a deliberate nudge gives a reference that passive views can't. Do you see acting on the system as a route past that?

Scott Allbright profil fotoğrafı

Scott Allbright3 ay önce

One thing I’m curious about: do you see latent-dynamics recovery and representation auditing as separate problems, or as two sides of the same problem? My intuition is that recovering the dynamics is step one, but measuring whether the learned space is stable, traversable, and regime-aware may become equally important for scientific use.

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Most “physics from video” papers replay the clip or do some re-decode LaGSplat learns a Lagrangian from a few seconds of monocular video, then lets you push on the object with a force that was never measured. It's so neat that the same latent q drives the Gaussians and the equations of motion. That’s why a click on the image becomes J^f

Most “physics from video” papers replay the clip or do some re-decode LaGSplat learns a Lagrangian from a few seconds of monocular video, then lets you push on the object with a force that was never measured. It's so neat that the same latent q drives the Gaussians and the equations of motion. That’s why a click on the image becomes J^f

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vittorio

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In 1927, Georges Lemaître derived a solution to Einstein’s field equations that shifted the paradigm of modern cosmology. While General Relativity described a curved fabric of space-time, Lemaître’s mathematics indicated that this fabric was not static but actively stretching. Professor Brian Cox explains that Lemaître was the first to recognize this expansion within the equations. By mentally reversing this process over billions of years, Lemaître deduced that the universe originated from a single, infinitesimal point. This "day without a yesterday" represents the birth of the Big Bang theory. Lemaître’s work proved that scientific inquiry and philosophical depth are dual paths toward understanding the truth of our cosmic origins. Credit: The Late Show with Stephen Colbert / Brian Cox

In 1927, Georges Lemaître derived a solution to Einstein’s field equations that shifted the paradigm of modern cosmology. While General Relativity described a curved fabric of space-time, Lemaître’s mathematics indicated that this fabric was not static but actively stretching. Professor Brian Cox explains that Lemaître was the first to recognize this expansion within the equations. By mentally reversing this process over billions of years, Lemaître deduced that the universe originated from a single, infinitesimal point. This "day without a yesterday" represents the birth of the Big Bang theory. Lemaître’s work proved that scientific inquiry and philosophical depth are dual paths toward understanding the truth of our cosmic origins. Credit: The Late Show with Stephen Colbert / Brian Cox

Cosmos Archive

17,985 görüntüleme • 5 ay önce

Today, we are introducing the Unified Balance Kit! It is a TypeScript SDK within App Kits that is built on top of Circle Gateway, a protocol that enables a unified USDC balance across supported blockchains and handles the underlying crosschain movement of funds when spending. Instead of stitching together different routing logics, developers can use this SDK to deposit, view, estimate, and spend from a unified USDC balance. Try it out yourself!

Today, we are introducing the Unified Balance Kit! It is a TypeScript SDK within App Kits that is built on top of Circle Gateway, a protocol that enables a unified USDC balance across supported blockchains and handles the underlying crosschain movement of funds when spending. Instead of stitching together different routing logics, developers can use this SDK to deposit, view, estimate, and spend from a unified USDC balance. Try it out yourself!

Elton Tay

12,139 görüntüleme • 5 ay önce