Mattia Ravasio

[Indra @IndraVahan]

computer use is getting solved, ladies and gentlemen

[öfa @sartvesistem]

th e fly does WHAT now????

[Mattia Ravasio @MattiaRavasio]

@deeperflows @arenaphysica Gotta ask Google for that

[Mattia Ravasio @MattiaRavasio]

The Fly is now an RF engineering intern.

@arenaphysica's Field Studio’s predictions stimulate its neurons. Neural activity sets the size of filter-design tweaks.

Can a fly help design a 15 GHz filter?

[News from Google @NewsFromGoogle]

For the first time, scientists have mapped the complete brain and central nervous system of an adult male fruit fly — a key model organism in science. 🪰

Working alongside HHMI Janelia Research Campus and the scientific community, @GoogleResearch scientists and researchers used AI to combine millions of 2D images into 3D neural shapes, reconstructing a record-breaking 166,000+ neurons. This foundational map of the adult male fruit fly brain can help accelerate our understanding of the brain, and is a major milestone in neuroscience.

[Arena Physica @arenaphysica]

🪰👀

[Mattia Ravasio @MattiaRavasio]

@zbell114 I’m not bit off to a strong start

[will bitsky @willbitsky]

2027 will be a good year for electronics + electromagnetism

cool new toys from @OpenAI (@0x50484244) and @arenaphysica (@_i_am_arya) today

[monolith𓆩❒𓆪 @0xMonolith]

>Astra launching
>nobody cares

>Atlas Field
>Huge

[Arena Physica @arenaphysica]

Since launching 60 hours ago, users have made 10,000 field predictions with Atlas Fields Studio 🎉

By popular request, we’ve made the app available on mobile (https://t.co/EoVt6KH2KH) - happy predicting!

[Nico Bykhovsky @BykhovskyNico]

A great way to know if you’re truly in a class of your own is if there’s no evals to compare yourself to

Congrats to a crazy team!

[Pratap Ranade @PratapRanade]

Today, we’re excited to unveil Heaviside-1, @arenaphysica's second generation foundation model for electromagnetism. 5 months since the release of Heaviside-0, Heaviside-1 is a crucial milestone on our quest to build EM superintelligence: a foundation model that understands EM across the spectrum, from RF through photonics, capable of designing the next generation of electronics.

Major updates:

- Heaviside-1 is >10x the size of Heaviside-0 (roughly the size of GPT-2), trained on 250k unique designs with >500B unique EM field samples.

- It runs 10^5 x faster than commercial solvers, with accuracy <1 dB.

- Heaviside-1 natively encodes 3D structures, their material properties, and excitation patterns as input.
It predicts full EM fields at arbitrary locations in space, not just downstream quantities like S-parameters.

- Most importantly, our bet on fields lets Heaviside generalize OOD proving that it’s learning real EM physics: Heaviside-1 generalizes outside of distribution very well, leaping from 0.99 dB to 0.53 dB S-parameter error.

Try it out in Atlas Fields Studio in beta today (link in replies). I personally had a lot of fun seeing how EM fields twist and curl around different circuits. I wish I had this when I was learning EM in college. I hope you enjoy it as much as we did.

[Arya Hezarkhani @_i_am_arya]

Today, we’re proud to unveil Heaviside-1, @arenaphysica's second generation foundation model for electromagnetism.

Five months since announcing Heaviside-0, today’s launch marks a critical milestone towards building electromagnetic superintelligence, a foundational intelligence that understands EM across the spectrum and is capable of designing next gen electromagnetically-governed systems, from RF to semiconductors to photonics.

Why we’re so excited about Heaviside-1, and what today’s announcement represents:

> It’s big: Heaviside-1 is a GPT-2-size model, roughly 10x the size of Heaviside-0 and trained on 250k unique designs with >500B unique EM field samples
> It’s fast: It generates predictions with 5 orders of magnitude acceleration relative to commercial solvers, with under 1dB of accuracy
> Full-3D Encoding: Heaviside-1 natively encodes 3D structures, their material properties, and excitation patterns as input, a major step towards generalizability across design spaces
> Understands the underlying physics primitive: Heaviside-1 generates full-3D near-fields at arbitrary locations, not just downstream quantities like s-parameters
> Generalization: Most importantly, our bet on fields as the primitive for Heaviside-1 generalizes OOD, proving that it’s learning real EM physics. Heaviside-1 generalizes outside of distribution incredibly well and better than Heaviside-0, down to 0.53 dB s-parameter error from 0.99 dB on Heaviside-0.

Read more about Heaviside-1 and the work that went into it in this write-up by our research team, @m__frei, @chris_m_bryant, @nathanmirman, @HaoLiu69889370, Tommaso, Roberto, Ruichen, Noah, Boyuan, and @Trevs_Dev, and our RF team @hk2532_harish, @anrfic, @suyangcn and Zhaoji.
https://t.co/XM2iwaWb5Z

[Arya Hezarkhani @_i_am_arya]

Today, we're announcing Heaviside, our foundation model for electromagnetism.

Trained on tens of millions of designs and over 20 years of proprietary simulation data, Heaviside predicts electromagnetic behavior from geometry in 13ms, which is 800,000x faster than a commercial solver.

Heaviside is not a language model, and it’s not a surrogate model. Heaviside marks a new class of foundation model for physics which understands the fundamental relationships between materials, the geometries and the electromagnetic fields they generate.

We’re releasing a research preview of Heaviside in Atlas RF Studio, an interactive agentic sandbox where you describe the EM behavior you want and the model generates the physical structure that produces it.

@arenaphysica , we believe the implications of this class of model extend well beyond RF, as the frontier of exquisite hardware is electromagnetically-governed: wireless communication, radar, power delivery, high-speed computing, and the interconnects inside every chip on earth.

In the months ahead, we’re excited to scale up Heaviside to broader frequency ranges, design spaces, and to support silicon-level designs, and deploy it with our closest partners and collaborators in service of their biggest design challenges.

If you’ve read our thesis, this is just Step 2 in our pursuit of electromagnetic superintelligence.

Read the full announcement and try Atlas RF Studio…tell us what you think: https://t.co/oCOsJQvF1h