AI for engineering.
MATAPPLY applies artificial intelligence and machine learning to engineering problems where prediction, automation, simulation acceleration and intelligent decision-making can create measurable value.
Not AI for the sake of AI. AI for engineering.
Engineering systems generate large amounts of technical data through simulation, testing, manufacturing and operation. The opportunity is not simply to add AI to an engineering workflow. It is to use engineering knowledge and AI together to create models that can predict behaviour, accelerate analysis, explore designs and support better technical decisions. MATAPPLY focuses on this intersection between engineering and computational intelligence.
Where AI can change the engineering workflow.
Prediction
Build models that predict engineering behaviour from historical simulation, experimental or operational data.
Simulation Acceleration
Use learned models and surrogate approaches to reduce the computational cost of repeated engineering evaluations.
Design Exploration
Explore large engineering design spaces using machine learning alongside computational optimization.
Engineering Automation
Automate repetitive engineering analysis, data processing and decision-support workflows.
Anomaly Detection
Identify unusual behaviour, patterns or potential failures within engineering and operational data.
Engineering Decision Support
Combine engineering models and data-driven methods to help engineers evaluate complex technical decisions.
Data. Engineering. Intelligence.
The value of Engineering AI comes from connecting data, engineering knowledge and computational models rather than treating them as separate systems.
Data
Simulation results, experiments, sensor data, CAD geometry, materials and engineering records.
Engineering Model
Physical relationships and engineering knowledge define the problem and its constraints.
AI Model
Machine learning models learn useful relationships within the available engineering data.
Validation
Predictions are evaluated against engineering requirements, known behaviour and independent data.
Decision
The resulting intelligence is connected to an engineering workflow or technical decision.
Apply AI where engineering data already exists.
Simulation Surrogates
Create fast approximations of expensive simulation models for design exploration, optimization and repeated analysis.
Predictive Engineering
Predict product or system behaviour before physical testing or full computational analysis.
Materials Intelligence
Use data-driven methods to investigate material behaviour, selection and performance relationships.
Generative Engineering
Explore engineering design alternatives using computational generation combined with performance constraints.
Engineering Knowledge Systems
Connect engineering documents, data and technical knowledge to intelligent retrieval and decision-support workflows.
Engineering Automation
Use intelligent computational workflows to reduce repetitive engineering tasks and accelerate technical analysis.
The technology follows the engineering problem.
MATAPPLY does not begin with a particular AI model and search for somewhere to use it. The engineering problem determines the appropriate computational approach.
Machine Learning
Models for prediction, classification, regression and engineering data analysis.
Surrogate Models
Fast learned approximations of expensive engineering simulations and physical models.
Optimization
Combine AI with computational optimization to search engineering design spaces.
Engineering Data
Organize simulation, experimental and product data so it can be used effectively by computational models.
AI matters when it helps solve a real problem.
Reduce Development Time
Accelerate repetitive simulation, analysis and engineering decision-making.
EXPLORE PROBLEM →Increase Performance
Use predictive and optimization models to explore better engineering designs.
EXPLORE PROBLEM →Reduce Cost
Reduce computational and development effort through intelligent engineering workflows.
EXPLORE PROBLEM →Reduce Weight
Use AI-supported design exploration to identify lighter engineering solutions.
EXPLORE PROBLEM →Improve Manufacturability
Incorporate manufacturing knowledge and constraints into intelligent design workflows.
EXPLORE PROBLEM →Engineering AI sits at the intersection of disciplines.
CAE & FEA
Simulation provides engineering data and physical insight for AI models.
Advanced Materials
AI can help investigate material behaviour, selection and performance.
Engineering Optimization
Optimization provides a natural framework for combining prediction and design exploration.
Product Engineering
AI becomes valuable when connected to real product development workflows.
Engineering AI across industrial domains.
Turn engineering data into engineering intelligence.
Start with the engineering problem, the available data and the decision that needs to improve. MATAPPLY can determine where AI, simulation and computational methods can create measurable engineering value.
START WITH A PROBLEM