Where AI Meets Physics to Engineer the Future.
PhysiqAI turns machine sensor data into explainable engineering decisions: real measurements, physics-based calculations, transparent rules, and clearly stated limits. The first demonstration analyses real vibration data from instrumented bearings.
A fault appears as a peak exactly where bearing physics says it should. The live demo shows this on real measurements.
From sensor signal to decision
Physics AI
Bearing kinematics predict where fault signatures must appear in the spectrum. Calculations are transparent and checkable.
Explore machine health →Physical AI
Sensor-driven decision support for rotating equipment, with scenario comparison of alarm settings and operating speed.
Compare scenarios →Industrial intelligence
Evidence, recommendation, limitations and an editable business scenario in one auditable report.
Review a decision →Photonics & quantum science
Educational explorations of lasers, optics and quantum concepts live on a companion site.
Visit quantum.viswanext.com →How the demonstration works
1 · Measured data
Real accelerometer recordings from healthy and seeded-fault bearings, with source and units shown.
2 · Physics
Defect frequencies from bearing geometry and shaft speed locate each fault signature.
3 · Analysis
Envelope-spectrum signal-to-noise ratios feed a transparent rule. Its misses and false alarms are shown, not hidden.
4 · Decision
Observation, evidence, suggested action, limits, and a downloadable report with version and result ID.
Where this applies
Any plant with rotating equipment: motors, pumps, fans, gearboxes and compressors. With larger fleets and simulation needs, accelerated computing opens up more.
What a customer pilot would measure: detection lead time, false alarms, and inspection hit-rate versus current practice. No savings are claimed here.
Path to enterprise and GPU acceleration
The demo runs on lightweight serverless compute with no GPU. As datasets, sensors and customers grow, heavier physics-informed models and simulation can move behind the same API to accelerated infrastructure (for example NVIDIA PhysicsNeMo or Omniverse), adopted only when measured workloads justify the cost.
See architecture & roadmap Request an engineering assessment