Your equipment operates in constantly changing conditions. Off-the-shelf solutions don't adapt. We build models that understand your equipment and your conditions.
Physics-Informed ML means you don't need massive datasets. We embed engineering knowledge directly into models to work with your existing sensor data.
No vendor lock-in. You receive the complete working product with full source code, documentation, and retraining procedures. Delivered in 12 weeks.
Physics-Informed Machine Learning for Changing Conditions
We solve the 'reality gap' in deep-sea digital twins by embedding physical laws (Navier-Stokes, Hertzian Contact, thermal dynamics) directly into the learning loop. This approach enables accurate predictions even with limited historical failure data.
From cutter-tooth wear estimation in turbid water to mud-pump seal failure prediction. We handle equipment operating in conditions that standard monitoring systems can't predict.
Real-time FEM surrogates for ladder fatigue analysis and multi-physics PINNs for deep-water winch drum integrity monitoring and thermal fade prediction. Specialized solutions for offshore cranes, pipelay tensioners, and heavy lifting systems.
Cutter suction dredgers, trailing suction hopper dredgers, slurry transport systems, and mud pumps operating in turbid, abrasive conditions.
Heavy lift cranes, pipelay tensioners, deep-water winches, jack-up rigs, and subsea construction vessels with complex dynamic loading.
Custom industrial machinery, rotating equipment, structural components, and high-value assets where unplanned downtime costs exceed €100k per day.
DP vessels, offshore wind support, classification society compliance, and fleet-wide condition monitoring for multi-asset operators.
We review your sensor data (open to NDA), confirm physics-based solvability, and provide a Go/No-Go recommendation. Zero cost & no obligation.
Physics-ML model creation with weekly sync meetings. We embed domain knowledge and validate against your operational data.
Deployment to your infrastructure, full documentation, and team training. You own the complete solution.
Critical offshore assets simply do not fail often enough to feed classical machine learning. Physics-informed models overcome this fundamental data scarcity problem.
A heavy lift fleet might see only a handful of major thruster or switchboard incidents over several years. Our models embed ISO 281 bearing life theory, Hertzian contact mechanics, and structural dynamics so you need far fewer failure examples.
Classification societies and insurers are skeptical of black-box AI. Our models output uncertainty bounds and the physical parameters driving each prediction, satisfying certification requirements.
Pure data-driven models often learn sensor drift as a new normal, creating cascading false alarms. Our hybrid validation layer cross-checks sensor readings against physical invariants like pump affinity laws.
Your offshore equipment operates in changing, unpredictable conditions
You have specific recurring failure problems
Standard monitoring systems aren't giving you the insights you need
You want to test if this works before investing
Every engagement begins with a 2-week feasibility assessment. We analyze your data, map the physics, and give you a Go/No-Go recommendation for development.
REQUEST FEASIBILITY ASSESSMENT →EA MULTUS B.V.
Serving Netherlands, Norway, and Northern Europe offshore equipment sector
Contact: erduan@eamultus.com