MATHWARE
From physics-based models to real-time optimization
BRIDGING SIMULATION AND REALITY ACROSS THE FULL SYSTEM LIFECYCLE
Creating digital twins
Building physics-based virtual replicas of complex systems. Grounded in thermal, mechanical and dynamical models. Enabling virtual testing, predictive control and what-if analysis.
Optimizing processes
Real-time modelling and optimization for complex systems. Combining physics understanding with data-driven learning. From design validation through operational optimization.
AI that understands the physics of your domain.
Hybrid methods combining machine learning with physics models. Explainable and certifiable – not a black box. Process optimization, predictive maintenance, quality control. Delivers actionable intelligence, not raw data.
Physics-informed neural networks
Hybrid thermal-mechanical models
Explainable yield models
Solving complex physical and mathematical problems through computation.
High-performance numerical methods for simulation, modelling and analysis of multi-physics systems. Efficient, accurate and scalable algorithms that turn intractable equations into actionable results.
Numerical simulation & PDE solvers
Reduced-order & surrogate modelling
High-performance computing
Inferring hidden system states and optimizing decisions.
Reconstruct what cannot be measured directly. Evaluate alternative interventions and control strategies. Support decisions with quantified uncertainty.
Bayesian inference
Kalman filtering
Finding the best decisions within complex constraints.
Formulating and solving optimization problems for design, planning and real-time control. Linear, nonlinear and mixed-integer methods that balance competing objectives and deliver provably good solutions.
Constrained & multi-objective optimization
Optimal control
Operations research
From model to operational system.
Data engineering, MLOps, edge AI and cloud deployment. Integration into production workflows and existing systems. Bridging prototype to industrial-grade solutions.
Edge AI deployment
MLOps pipelines
System integration
AI that understands the physics of your domain.
Hybrid methods combining machine learning with physics models. Explainable and certifiable – not a black box. Process optimization, predictive maintenance, quality control. Delivers actionable intelligence, not raw data.
Physics-informed neural networks
Hybrid thermal-mechanical models
Explainable yield models
Solving complex physical and mathematical problems through computation.
High-performance numerical methods for simulation, modelling and analysis of multi-physics systems. Efficient, accurate and scalable algorithms that turn intractable equations into actionable results.
Numerical simulation & PDE solvers
Reduced-order & surrogate modelling
High-performance computing
Inferring hidden system states and optimizing decisions.
Reconstruct what cannot be measured directly. Evaluate alternative interventions and control strategies. Support decisions with quantified uncertainty.
Bayesian inference
Kalman filtering
Finding the best decisions within complex constraints.
Formulating and solving optimization problems for design, planning and real-time control. Linear, nonlinear and mixed-integer methods that balance competing objectives and deliver provably good solutions.
Constrained & multi-objective optimization
Optimal control
Operations research
From model to operational system.
Data engineering, MLOps, edge AI and cloud deployment. Integration into production workflows and existing systems. Bridging prototype to industrial-grade solutions.
Edge AI deployment
MLOps pipelines
System integration
WHAT IS YOUR CHALLENGE?
Share your challenge with us, and we'll help bring your high-tech solution to life.
Contact us
