CAAPI develops high-fidelity computational technologies by tightly integrating first-principles physics with scientific machine learning. Our approach prioritises accuracy, robustness, and deployability in real-world aerospace and defence systems.
At the core of CAAPI’s technology stack are physics-based numerical solvers derived from governing conservation laws. These solvers are designed to accurately capture nonlinear phenomena such as shocks, multiphase interactions, and strongly coupled flow-structure effects.
Emphasis is placed on high-order spatial and temporal discretisation, stability in extreme regimes, and extensibility across problem classes.
CAAPI employs physics-informed and physics-guided machine learning methodologies that embed governing equations, constraints, and invariants directly into learning architectures.
This enables data-efficient training, improved generalisation beyond training regimes, and physically consistent predictions under sparse or noisy data conditions.
To address the computational cost of high-fidelity simulations, CAAPI develops hybrid solver architectures that couple numerical solvers with learned surrogate components.
These hybrids retain physical fidelity while achieving orders-of-magnitude reductions in runtime, enabling rapid design iteration, optimisation, and real-time decision support.
CAAPI’s trajectory modelling frameworks address both interior and exterior ballistics, incorporating propulsion dynamics, aerodynamics, environmental effects, and uncertainty propagation.
Physics-based solvers are augmented with data-driven acceleration to enable fast and reliable trajectory prediction suitable for simulation, analysis, and decision-support applications.
Building on its modelling capabilities, CAAPI develops decision-support tools that integrate simulation outputs, reduced-order models, and uncertainty-aware predictions.
These systems are designed to support planning, analysis, and engineering workflows in complex operational contexts, while remaining transparent and explainable to domain experts.
CAAPI places strong emphasis on verification, validation, and traceability. Models are evaluated against analytical solutions, high-fidelity reference simulations, and experimental or field data where available.
Deployment considerations — including robustness, interpretability, and integration with existing workflows — are treated as first-class design constraints.