- Governing relationships and states
- Parameters and excitation cases
- Calibration and reference behaviour
Engineering / Physics modelling
Build models that explain system behaviour.
Physics based and reduced order modelling for design exploration, controls, digital twins and predictive applications. Models are selected and validated according to their intended decision authority.
Service overview
Preserve the governing engineering relationships while achieving the speed and fidelity required by the use case.
Physics based and reduced order modelling for design exploration, controls, digital twins and predictive applications. Models are selected and validated according to their intended decision authority.
- Service model
- Use focused specialist support for a defined gap, or assemble a multidisciplinary team around a complete workstream. The structure follows the interfaces and decisions in the scope.
- Delivery
- Choose a project, dedicated team, hybrid engagement or managed service. Milestones, client responsibilities and acceptance criteria are agreed before execution.
- Control
- Scope, assumptions, evidence, ownership and review rhythm remain visible. Changes and unresolved risks are recorded with an accountable decision owner.
- System dynamics
- Modelica style modelling
- Reduced order methods
- Model formulation and units
- Numerical and parameter evidence
- Validity range and integration contract
- Simulation and CAE
- Digital twins
- AI and automation
Lifecycle positionPurpose
Define the decision and required model authority.
Core capabilities
Capabilities included.
Each capability explains the work itself, how it connects with the surrounding product or operating system, and the evidence needed to make the result usable.
System modelling
Represent interacting mechanical, thermal, fluid, electrical and control behaviour.
Practical coverage
Create a system level view across subsystem boundaries.
Reduced order models
Derive faster representations from detailed simulation or measured behaviour.
Practical coverage
Enable rapid exploration and runtime applications.
Co simulation
Connect domain models through controlled interface and time coupling logic.
Practical coverage
Study cross domain system behaviour without hiding interfaces.
Parameter estimation
Calibrate uncertain model parameters against measured or reference data.
Practical coverage
Separate model form error from uncertain inputs.
Surrogate modelling
Create computationally efficient approximations across defined design spaces.
Practical coverage
Accelerate optimisation while making validity limits explicit.
Physics informed AI
Combine governing constraints and data driven learning for prediction.
Practical coverage
Retain engineering review, uncertainty and validation controls.
Solutions
Solutions for common delivery needs.
The starting point can be a technical problem, a capacity gap or the need for a complete delivery workstream. CX24 first clarifies the current state, desired outcome and decision ownership.
Detailed simulation is too slow for design exploration.
How CX24 respondsDefine the decision and required model authority. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Controls development needs an executable plant model.
How CX24 respondsSelect states, physics, assumptions and interfaces. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
A digital twin requires a validated runtime representation.
How CX24 respondsEstimate parameters against relevant evidence. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Data is limited and physical relationships must guide prediction.
How CX24 respondsTest accuracy, robustness and limits. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Delivery approach
A clear path from scope to evidence.
The exact gates change by service, but ownership, review and measurable outputs stay explicit. Each stage establishes the information needed to enter the next one responsibly.
Purpose
Define the decision and required model authority.
Stage outcomeModel specification is prepared or updated before the work advances.Formulate
Select states, physics, assumptions and interfaces.
Stage outcomeExecutable model is prepared or updated before the work advances.Calibrate
Estimate parameters against relevant evidence.
Stage outcomeCalibration evidence is prepared or updated before the work advances.Validate
Test accuracy, robustness and limits.
Stage outcomeValidation record is prepared or updated before the work advances.Deploy
Package the model with monitoring and version control.
Stage outcomeAgreed next stage evidence is prepared or updated before the work advances.What you receive
Typical deliverables.
Deliverables are adapted to the client environment and agreed acceptance criteria. The aim is to leave behind usable engineering, technology or operating capability, not presentation material alone.
Model specification
Purpose, variables, equations, assumptions and validity range.
Assumptions, ownership, dependencies and the agreed review status are recorded with the output.Executable model
Versioned model implementation and interfaces.
Working files, source information and revision status are organised so the client team can continue using them.Calibration evidence
Data, parameter estimates and residual analysis.
Results include the relevant checks, open issues, limitations and approval evidence, not only the final conclusion.Validation record
Accuracy, limits, uncertainty and deployment controls.
Final handover identifies accepted scope, residual risk, next actions and the accountable owner for each action.Technology and methods
System dynamicsModelica style modellingReduced order methodsScientific PythonOptimisationPhysics informed ML
Evidence clients can review before acceptance.
Proof is defined through the engagement itself: accepted outputs, a visible decision trail, agreed measures and a usable handover.
Accepted outputs
Deliverables are mapped to agreed criteria, version status, accountable owners and review decisions.
Decision trail
Inputs, assumptions, interfaces, changes, exceptions and approvals remain connected to the work.
Delivery measures
Progress, quality, risk, backlog or service measures are selected for the actual engagement.
Usable continuity
Working files, source information, runbooks and handover actions leave the client able to continue.
Client names, project details and outcome claims are published only with permission. Evidence for a specific engagement is confirmed through the agreed scope, reviews and acceptance records.
Choose a delivery structure that fits the responsibility.
Team shape, governance, commercial structure and acceptance are matched to the outcome CX24 is asked to deliver.
Defined project
Bounded scope, milestones, deliverables and acceptance criteria for a specific outcome.
Dedicated team
Stable specialist capacity integrated with client leadership, standards and delivery rhythms.
Delivery centre
Multidisciplinary capacity with named governance, shared methods and transparent reporting.
Managed service
Recurring responsibility operated against controls, service levels and improvement measures.
Build · Enable · Operate