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Source Sequence builds physics-grounded artificial intelligence — reliable, trustworthy, and interpretable prediction and decision systems for the noisy, data-scarce reality of industry.
Inject physical laws into the model to deliver reliable prediction and uncertainty quantification on small, low-quality datasets.
Independent physical-consistency review, boundary testing, and trustworthiness assessment for AI outputs — a gatekeeper for the physical world.
Built on real solvers and mechanistic models: surrogate-model acceleration, physically consistent synthetic data, and simulation environments.
Remaining-life and fault prediction for pumps, compressors, motors, and gearboxes.
The cost of a single unplanned shutdown is clearly quantifiable; one flagship deployment replicates across a group.
Degradation prediction and maintenance decision support for blades and gearboxes from SCADA data.
High maintenance cost and poor data quality — where small-sample, noise-robust modeling shines most.
State-of-health, remaining-life, and residual-value assessment for storage and traction batteries.
Tied directly to asset residual value and insurance pricing; deliverable as a lightweight pay-per-use service.
Prediction and optimization of key process nodes such as endpoint composition and continuous-casting quality.
Cost is measured per heat and clearly visible; fits the industry's dedicated budget cycle for intelligent retrofits.
The first open-source proof of our Physics-Grounded AI philosophy — real electromagnetic solvers and AI optimization, reimagining antenna design.
A physics-grounded equipment-reliability prediction system delivered in energy and industrial settings — small-sample modeling, uncertainty quantification, and deployment in localized environments.
From reliable prediction, to autonomous decision-making, to embodied intelligence.
AI that truly understands the physical world is the only AI that can truly enter it.
Source Sequence was founded in Hangzhou in 2025 by a group of researchers and engineers trained at leading universities across China, the United Kingdom, and the United States. We came together around a simple thesis: the way to make AI reliable in the physical world is to ground it in physics—not to feed it ever more data.
We build physics-grounded AI for the data-scarce, noisy reality of industry: reliable prediction, independent physical validation, and high-fidelity simulation. We are research-first—we publish what we build and collaborate with universities. Our flagship open-source proof, YAF, reimagines antenna design with real electromagnetic solvers and AI.
We hire researchers, engineers, and operators who care about doing fundamental work. Open positions are posted as we grow—but we're always open to talking with exceptional people.
Lead the design and characterization of next-generation dielectric waveguide systems.
ApplyDevelop neural architectures for real-time control of reconfigurable radio systems.
ApplyBuild the tools and interfaces that make our research legible to the world.
ApplyHardware project, research collaboration, or just a curious question — drop us a line and we'll respond within two business days.
Source Sequence Technology · Hangzhou, China