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. arenaphysica.com/publication…