According to a recent LinkedIn post from Nullspace, the company is showcasing its electromagnetic (EM) solver and AI-assisted workflow using the NASA PEC Almond monostatic radar cross-section benchmark. The post describes completing eight simulation runs across four frequencies and two polarizations in about 45 minutes on a laptop with 8 cores and 32 GB of RAM.
The company’s LinkedIn post highlights that Nullspace EM reportedly matches a University of Texas at Austin reference simulation across the benchmark, with each radar cross-section value derived from a full-wave Method of Moments solution rather than surrogate models. The post emphasizes that roughly 35 minutes were devoted to physics computation, while about 10 minutes involved an AI agent generating mesh scripts, solver inputs, running cases, extracting data, and producing error metrics and plots.
The post suggests that Nullspace’s differentiation lies in combining a Python-native EM solver with large language models such as Claude Code to automate simulation workflows. This framing positions the product as a tool to reduce engineering “busywork,” potentially increasing productivity for RF and computational electromagnetics teams without claiming faster or more accurate physics.
For investors, this demonstration may indicate early technical validation and a practical integration path between AI and physics-based solvers, which could be attractive in defense, aerospace, and advanced communications markets. If the approach scales beyond benchmarks to complex commercial use cases, Nullspace could capture niche share in EM simulation and AI-enabled engineering tools, though the post does not address pricing, customer traction, or revenue impact.