Enhanced Decision Making for Engineering Applications

We help companies make critical decisions during design, development and operation of complex engineering systems.

Scientific Computing

Scientific Computing is one of the most critical disciplines that focuses on simulating physical phenomena to gain a better insight towards real-life problems using the high performance computing systems.

Domain-Aware Machine Learning

Domain-Aware Machine Learning is a novel approach that integrates scientific computing and traditional machine learning algorithms to match the level of performance and reliability needed by complex and critical systems.

Enhanced Decision Making

Enhanced Decision Making builds upon traditional decision making methods by using recent advances in Artificial Intelligence to provide reliable metrics for systems with large degrees of freedom.

Scientific Computing

Using High Performance Computing systems we perform Computational Fluid Dynamics simulations in the fields of:

  • Aerodynamics

  • Aerothermodynamics

  • Space Propulsion Systems

  • Space Environmental Awareness

  • Renewable Energy Systems

  • Turbomachinery

Domain-Aware Machine Learning

In order to make the best of small datasets from scientific computing or experiments, we develop novel machine learning algorithms that implements the physical laws into the training stage resulting in;

  • Better performance with smaller datasets

  • Reduction in cost related to computational and experimental analysis

  • Models that are explainable and interpretable by domain experts

Enhanced Decision Making

By making use of existing data/information or generating new ones through simulations, we develop customized frameworks to perform:

  • Optimization

  • Sensitivity Analysis

  • Uncertainty Quantification

  • Advanced Control System Development

  • Surrogate Modeling

Application Areas

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