Updates
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Web Summit Vancouver 2026: Niva brings Manifold's runtime physics to cross-industry audience
Niva attended Web Summit Vancouver 2026 (May 12-14), introducing Manifold to a cross-industry audience of founders, investors, and enterprise buyers after a satellite-industry debut at SATShow in March. Conversations clustered around manufacturing, robotics, and materials discovery, with the runtime-versus-design-time distinction and a head-to-head comparison against Physical Intelligence's π0.5 as the recurring anchors.
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Closing the coupling chain: Applying Manifold's runtime physics to orbital state prediction
Object-specific orbital state prediction rests on a physics chain from material exposure through gas-surface interaction to ballistic behavior to orbit realism. The literature supports each link. It does not close the chain as a continuous runtime process. Niva's Manifold platform resolves the chain end-to-end at microsecond solver latency with deterministic commits - a world's first.
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Demo: Satellite ADCS, bus shutdown prediction with coupled physics warm start recovery
Manifold predicts a satellite's attitude, orbit, and thermal state through a complete bus shutdown window, then hands the warm-start prior to ADCS recovery. Conventional cold-start recovery takes 5 to 12 minutes, sometimes longer. Manifold cuts it to under 2 minutes. Try it on the Demos page.
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SATShow 2026: physics-native AI meets the satellite industry
Four days at SATShow 2026 in Washington, DC. Conversations with operators, manufacturers, ground segment providers, primes, and component suppliers across satellite, RF, and space domains. Deterministic AI that operates at runtime, sensor-fused, on edge hardware, without training data, while retaining exceptional accuracy.
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Demo: Satellite Thermal Analysis, real-time orbital thermal at 43ms
Manifold computes the full multi-physics thermal balance for a CubeSat in low Earth orbit, solar heating, Earth IR, albedo, internal dissipation, and radiative cooling, in 43 milliseconds. Thermal Desktop and ESATAN take 4 to 8 hours for the same calculation. Try it on the Demos page.
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Demo: Engineering Sweep, real-time polymer certification at millisecond latency
Manifold computes the full physics prediction for a recycled polymer composite part, stress, temperature, displacement, crystallinity, and fire resistance, in 143 milliseconds. Conventional offline simulation often takes hours to days for the same calculation. Try it on the Demos page.
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Why LLMs can't do spatial reasoning at scale, and what models can
Across 11 frontier LLM variants tested on grid navigation, every model collapsed to 0% by the size of a bathroom. Manifold and classical graph search hit 100% on a 50,000×50,000 grid (2.5 billion cells, 25 square kilometers at 10cm resolution) on a single consumer GPU. The phase transition is architectural, not a training problem.
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Manifold on Theory of Space: 97% accuracy with structured sensor input, where frontier LLMs collapse to 4-10%
On Stanford and Northwestern's Theory of Space benchmark (ICLR 2026), Manifold reached 97% accuracy with structured sensor input, against 72% for GPT-5.2 and 66% for Claude-4.5 Sonnet on text input. When the same structured sensor data was given to the LLMs, their accuracy dropped to 4-10%. The first non-LLM system evaluated on the benchmark.
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Head-to-head: Manifold vs Physical Intelligence on identical hardware
On identical UR5e hardware and the same contact-rich manipulation task, Manifold reached 98% success against 14% for Physical Intelligence's π0.5. Zero-shot, no training. Manifold ran at 60 Hz; π0.5 at 4.4 Hz.
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Predicting coastal flooding in the Netherlands with satellite data
Using historic ESA Sentinel-1 and Sentinel-2 imagery over a 10km stretch of the Dutch coast, Manifold processed 59 SAR and 13 optical observations through the same architecture that runs our robotics work, producing roughly 18 days of lead time on coastal anomaly signals.
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